From d8c5791b3452bf52f28aebcdc31be5fb53a9f658 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=EC=A1=B0=ED=95=B4=EC=B0=BD?=
 <hc.cho@johaechang-ui-MacBookPro-2.local>
Date: Fri, 3 Oct 2025 17:04:35 +0900
Subject: [PATCH 1/6] update

---
 book/scm/causal_model.png           |  Bin 0 -> 83821 bytes
 book/scm/graph_based_approach.ipynb | 1105 +++++++++++++++++++++++++++
 book/scm/overview.md                |    9 +
 3 files changed, 1114 insertions(+)
 create mode 100644 book/scm/causal_model.png
 create mode 100644 book/scm/graph_based_approach.ipynb
 create mode 100644 book/scm/overview.md

diff --git a/book/scm/causal_model.png b/book/scm/causal_model.png
new file mode 100644
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diff --git a/book/scm/graph_based_approach.ipynb b/book/scm/graph_based_approach.ipynb
new file mode 100644
index 0000000..ffe2870
--- /dev/null
+++ b/book/scm/graph_based_approach.ipynb
@@ -0,0 +1,1105 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "de707838",
+   "metadata": {},
+   "source": [
+    "# Graph based approach"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d1e7ed3",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd8821be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: dowhy==0.13 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (0.13)\n",
+      "Requirement already satisfied: causaldata in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (0.1.5)\n",
+      "Requirement already satisfied: econml in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (0.16.0)\n",
+      "Requirement already satisfied: scikit-learn in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (1.6.1)\n",
+      "Requirement already satisfied: causal-learn>=0.1.3.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (0.1.4.3)\n",
+      "Requirement already satisfied: cvxpy<1.5 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (1.4.4)\n",
+      "Requirement already satisfied: cython>=3.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (3.1.4)\n",
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+      "Requirement already satisfied: networkx>=3.3 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (3.5)\n",
+      "Requirement already satisfied: numba>=0.59 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (0.60.0)\n",
+      "Requirement already satisfied: numpy>1.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (2.0.2)\n",
+      "Requirement already satisfied: pandas>1.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (2.2.2)\n",
+      "Requirement already satisfied: scipy<=1.15.3 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (1.15.3)\n",
+      "Requirement already satisfied: statsmodels>=0.14 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (0.14.5)\n",
+      "Requirement already satisfied: sympy>=1.10.1 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (1.14.0)\n",
+      "Requirement already satisfied: tqdm>=4.64.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (4.67.1)\n",
+      "Requirement already satisfied: osqp>=0.6.2 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from cvxpy<1.5->dowhy==0.13) (1.0.4)\n",
+      "Requirement already satisfied: ecos>=2 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from cvxpy<1.5->dowhy==0.13) (2.0.14)\n",
+      "Requirement already satisfied: clarabel>=0.5.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from cvxpy<1.5->dowhy==0.13) (0.11.1)\n",
+      "Requirement already satisfied: scs>=3.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from cvxpy<1.5->dowhy==0.13) (3.2.8)\n",
+      "Requirement already satisfied: pybind11 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from cvxpy<1.5->dowhy==0.13) (3.0.1)\n",
+      "Requirement already satisfied: sparse in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from econml) (0.17.0)\n",
+      "Requirement already satisfied: shap<0.49.0,>=0.38.1 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from econml) (0.48.0)\n",
+      "Requirement already satisfied: lightgbm in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from econml) (4.6.0)\n",
+      "Requirement already satisfied: packaging in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from econml) (25.0)\n",
+      "Requirement already satisfied: threadpoolctl>=3.1.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from scikit-learn) (3.6.0)\n",
+      "Requirement already satisfied: slicer==0.0.8 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from shap<0.49.0,>=0.38.1->econml) (0.0.8)\n",
+      "Requirement already satisfied: cloudpickle in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from shap<0.49.0,>=0.38.1->econml) (3.1.1)\n",
+      "Requirement already satisfied: typing-extensions in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from shap<0.49.0,>=0.38.1->econml) (4.15.0)\n",
+      "Requirement already satisfied: graphviz in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from causal-learn>=0.1.3.0->dowhy==0.13) (0.21)\n",
+      "Requirement already satisfied: matplotlib in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from causal-learn>=0.1.3.0->dowhy==0.13) (3.10.6)\n",
+      "Requirement already satisfied: pydot in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from causal-learn>=0.1.3.0->dowhy==0.13) (4.0.1)\n",
+      "Requirement already satisfied: momentchi2 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from causal-learn>=0.1.3.0->dowhy==0.13) (0.1.8)\n",
+      "Requirement already satisfied: cffi in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from clarabel>=0.5.0->cvxpy<1.5->dowhy==0.13) (2.0.0)\n",
+      "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from numba>=0.59->dowhy==0.13) (0.43.0)\n",
+      "Requirement already satisfied: jinja2 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from osqp>=0.6.2->cvxpy<1.5->dowhy==0.13) (3.1.6)\n",
+      "Requirement already satisfied: setuptools in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from osqp>=0.6.2->cvxpy<1.5->dowhy==0.13) (80.9.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from pandas>1.0->dowhy==0.13) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from pandas>1.0->dowhy==0.13) (2025.2)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from pandas>1.0->dowhy==0.13) (2025.2)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas>1.0->dowhy==0.13) (1.17.0)\n",
+      "Requirement already satisfied: patsy>=0.5.6 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from statsmodels>=0.14->dowhy==0.13) (1.0.1)\n",
+      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from sympy>=1.10.1->dowhy==0.13) (1.3.0)\n",
+      "Requirement already satisfied: pycparser in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from cffi->clarabel>=0.5.0->cvxpy<1.5->dowhy==0.13) (2.23)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from jinja2->osqp>=0.6.2->cvxpy<1.5->dowhy==0.13) (3.0.3)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from matplotlib->causal-learn>=0.1.3.0->dowhy==0.13) (1.3.3)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from matplotlib->causal-learn>=0.1.3.0->dowhy==0.13) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from matplotlib->causal-learn>=0.1.3.0->dowhy==0.13) (4.60.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from matplotlib->causal-learn>=0.1.3.0->dowhy==0.13) (1.4.9)\n",
+      "Requirement already satisfied: pillow>=8 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from matplotlib->causal-learn>=0.1.3.0->dowhy==0.13) (11.3.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from matplotlib->causal-learn>=0.1.3.0->dowhy==0.13) (3.2.4)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install dowhy==0.13 causaldata econml scikit-learn\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "from causaldata import nhefs\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
+    "from sklearn.ensemble import GradientBoostingRegressor\n",
+    "\n",
+    "from dowhy import CausalModel\n",
+    "from econml.inference import BootstrapInference\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "102336cd",
+   "metadata": {},
+   "source": [
+    "- 금연이 체중 변화에 미치는 효과를 추정하고자 합니다.  \n",
+    "- 이를 위해 [**NHEFS (National Health and Nutrition Examination Survey – Epidemiologic Follow-up Study)**](https://rdrr.io/cran/causaldata/man/nhefs.html) 데이터를 활용합니다.  \n",
+    "- 이 데이터셋은 1971년 당시 흡연자였던 성인 코호트를 1982년까지 추적한 패널 자료로,  \n",
+    "금연 여부(`qsmk`, 1=금연, 0=계속 흡연), 체중 변화(`wt82_71`), 연령, 성별, 신체 활동 수준, 운동 습관 등 다양한 변수를 포함하고 있습니다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "3f63bbe8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of Data:\n",
+      "(1629, 67)\n",
+      "Variable Names:\n",
+      "Index(['seqn', 'qsmk', 'death', 'yrdth', 'modth', 'dadth', 'sbp', 'dbp', 'sex',\n",
+      "       'age', 'race', 'income', 'marital', 'school', 'education', 'ht', 'wt71',\n",
+      "       'wt82', 'wt82_71', 'birthplace', 'smokeintensity', 'smkintensity82_71',\n",
+      "       'smokeyrs', 'asthma', 'bronch', 'tb', 'hf', 'hbp', 'pepticulcer',\n",
+      "       'colitis', 'hepatitis', 'chroniccough', 'hayfever', 'diabetes', 'polio',\n",
+      "       'tumor', 'nervousbreak', 'alcoholpy', 'alcoholfreq', 'alcoholtype',\n",
+      "       'alcoholhowmuch', 'pica', 'headache', 'otherpain', 'weakheart',\n",
+      "       'allergies', 'nerves', 'lackpep', 'hbpmed', 'boweltrouble', 'wtloss',\n",
+      "       'infection', 'active', 'exercise', 'birthcontrol', 'pregnancies',\n",
+      "       'cholesterol', 'hightax82', 'price71', 'price82', 'tax71', 'tax82',\n",
+      "       'price71_82', 'tax71_82', 'id', 'censored', 'older'],\n",
+      "      dtype='object')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>seqn</th>\n",
+       "      <th>qsmk</th>\n",
+       "      <th>death</th>\n",
+       "      <th>yrdth</th>\n",
+       "      <th>modth</th>\n",
+       "      <th>dadth</th>\n",
+       "      <th>sbp</th>\n",
+       "      <th>dbp</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>race</th>\n",
+       "      <th>income</th>\n",
+       "      <th>marital</th>\n",
+       "      <th>school</th>\n",
+       "      <th>education</th>\n",
+       "      <th>ht</th>\n",
+       "      <th>wt71</th>\n",
+       "      <th>wt82</th>\n",
+       "      <th>wt82_71</th>\n",
+       "      <th>birthplace</th>\n",
+       "      <th>smokeintensity</th>\n",
+       "      <th>smkintensity82_71</th>\n",
+       "      <th>smokeyrs</th>\n",
+       "      <th>asthma</th>\n",
+       "      <th>bronch</th>\n",
+       "      <th>tb</th>\n",
+       "      <th>hf</th>\n",
+       "      <th>hbp</th>\n",
+       "      <th>pepticulcer</th>\n",
+       "      <th>colitis</th>\n",
+       "      <th>hepatitis</th>\n",
+       "      <th>chroniccough</th>\n",
+       "      <th>hayfever</th>\n",
+       "      <th>diabetes</th>\n",
+       "      <th>polio</th>\n",
+       "      <th>tumor</th>\n",
+       "      <th>nervousbreak</th>\n",
+       "      <th>alcoholpy</th>\n",
+       "      <th>alcoholfreq</th>\n",
+       "      <th>alcoholtype</th>\n",
+       "      <th>alcoholhowmuch</th>\n",
+       "      <th>pica</th>\n",
+       "      <th>headache</th>\n",
+       "      <th>otherpain</th>\n",
+       "      <th>weakheart</th>\n",
+       "      <th>allergies</th>\n",
+       "      <th>nerves</th>\n",
+       "      <th>lackpep</th>\n",
+       "      <th>hbpmed</th>\n",
+       "      <th>boweltrouble</th>\n",
+       "      <th>wtloss</th>\n",
+       "      <th>infection</th>\n",
+       "      <th>active</th>\n",
+       "      <th>exercise</th>\n",
+       "      <th>birthcontrol</th>\n",
+       "      <th>pregnancies</th>\n",
+       "      <th>cholesterol</th>\n",
+       "      <th>hightax82</th>\n",
+       "      <th>price71</th>\n",
+       "      <th>price82</th>\n",
+       "      <th>tax71</th>\n",
+       "      <th>tax82</th>\n",
+       "      <th>price71_82</th>\n",
+       "      <th>tax71_82</th>\n",
+       "      <th>id</th>\n",
+       "      <th>censored</th>\n",
+       "      <th>older</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>233.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>175.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>174.1875</td>\n",
+       "      <td>79.04</td>\n",
+       "      <td>68.946040</td>\n",
+       "      <td>-10.093960</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>197.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.183594</td>\n",
+       "      <td>1.739990</td>\n",
+       "      <td>1.102295</td>\n",
+       "      <td>0.461975</td>\n",
+       "      <td>0.443787</td>\n",
+       "      <td>0.640381</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>235.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>123.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>159.3750</td>\n",
+       "      <td>58.63</td>\n",
+       "      <td>61.234970</td>\n",
+       "      <td>2.604970</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>-10.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>301.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.346680</td>\n",
+       "      <td>1.797363</td>\n",
+       "      <td>1.364990</td>\n",
+       "      <td>0.571899</td>\n",
+       "      <td>0.549316</td>\n",
+       "      <td>0.792969</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>244.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>115.0</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>168.5000</td>\n",
+       "      <td>56.81</td>\n",
+       "      <td>66.224486</td>\n",
+       "      <td>9.414486</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>-14.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.569580</td>\n",
+       "      <td>1.513428</td>\n",
+       "      <td>0.551270</td>\n",
+       "      <td>0.230988</td>\n",
+       "      <td>0.056198</td>\n",
+       "      <td>0.320251</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>245.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>148.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>170.1875</td>\n",
+       "      <td>59.42</td>\n",
+       "      <td>64.410117</td>\n",
+       "      <td>4.990117</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>174.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.506592</td>\n",
+       "      <td>1.451904</td>\n",
+       "      <td>0.524902</td>\n",
+       "      <td>0.219971</td>\n",
+       "      <td>0.054794</td>\n",
+       "      <td>0.304993</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>252.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>181.8750</td>\n",
+       "      <td>87.09</td>\n",
+       "      <td>92.079251</td>\n",
+       "      <td>4.989251</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>216.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.346680</td>\n",
+       "      <td>1.797363</td>\n",
+       "      <td>1.364990</td>\n",
+       "      <td>0.571899</td>\n",
+       "      <td>0.549316</td>\n",
+       "      <td>0.792969</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    seqn  qsmk  death  yrdth  modth  dadth    sbp   dbp sex   age race  \\\n",
+       "0  233.0   0.0    0.0    NaN    NaN    NaN  175.0  96.0   0  42.0    1   \n",
+       "1  235.0   0.0    0.0    NaN    NaN    NaN  123.0  80.0   0  36.0    0   \n",
+       "2  244.0   0.0    0.0    NaN    NaN    NaN  115.0  75.0   1  56.0    1   \n",
+       "3  245.0   0.0    1.0   85.0    2.0   14.0  148.0  78.0   0  68.0    1   \n",
+       "4  252.0   0.0    0.0    NaN    NaN    NaN  118.0  77.0   0  40.0    0   \n",
+       "\n",
+       "   income  marital  school education        ht   wt71       wt82    wt82_71  \\\n",
+       "0    19.0      2.0     7.0         1  174.1875  79.04  68.946040 -10.093960   \n",
+       "1    18.0      2.0     9.0         2  159.3750  58.63  61.234970   2.604970   \n",
+       "2    15.0      3.0    11.0         2  168.5000  56.81  66.224486   9.414486   \n",
+       "3    15.0      3.0     5.0         1  170.1875  59.42  64.410117   4.990117   \n",
+       "4    18.0      2.0    11.0         2  181.8750  87.09  92.079251   4.989251   \n",
+       "\n",
+       "   birthplace  smokeintensity  smkintensity82_71  smokeyrs  asthma  bronch  \\\n",
+       "0        47.0            30.0              -10.0      29.0     0.0     0.0   \n",
+       "1        42.0            20.0              -10.0      24.0     0.0     0.0   \n",
+       "2        51.0            20.0              -14.0      26.0     0.0     0.0   \n",
+       "3        37.0             3.0                4.0      53.0     0.0     0.0   \n",
+       "4        42.0            20.0                0.0      19.0     0.0     0.0   \n",
+       "\n",
+       "    tb   hf  hbp  pepticulcer  colitis  hepatitis  chroniccough  hayfever  \\\n",
+       "0  0.0  0.0  1.0          1.0      0.0        0.0           0.0       0.0   \n",
+       "1  0.0  0.0  0.0          0.0      0.0        0.0           0.0       0.0   \n",
+       "2  0.0  0.0  0.0          0.0      0.0        0.0           0.0       1.0   \n",
+       "3  0.0  0.0  1.0          0.0      0.0        0.0           0.0       0.0   \n",
+       "4  0.0  0.0  0.0          0.0      0.0        0.0           0.0       0.0   \n",
+       "\n",
+       "   diabetes  polio  tumor  nervousbreak  alcoholpy  alcoholfreq  alcoholtype  \\\n",
+       "0       1.0    0.0    0.0           0.0        1.0          1.0          3.0   \n",
+       "1       0.0    0.0    0.0           0.0        1.0          0.0          1.0   \n",
+       "2       0.0    0.0    1.0           0.0        1.0          3.0          4.0   \n",
+       "3       0.0    0.0    0.0           0.0        1.0          2.0          3.0   \n",
+       "4       0.0    0.0    0.0           0.0        1.0          2.0          1.0   \n",
+       "\n",
+       "   alcoholhowmuch  pica  headache  otherpain  weakheart  allergies  nerves  \\\n",
+       "0             7.0   0.0       1.0        0.0        0.0        0.0     0.0   \n",
+       "1             4.0   0.0       1.0        0.0        0.0        0.0     0.0   \n",
+       "2             NaN   0.0       1.0        1.0        0.0        0.0     1.0   \n",
+       "3             4.0   0.0       0.0        1.0        1.0        0.0     0.0   \n",
+       "4             2.0   0.0       1.0        0.0        0.0        0.0     0.0   \n",
+       "\n",
+       "   lackpep  hbpmed  boweltrouble  wtloss  infection active exercise  \\\n",
+       "0      0.0     1.0           0.0     0.0        0.0      0        2   \n",
+       "1      0.0     0.0           0.0     0.0        1.0      0        0   \n",
+       "2      0.0     0.0           0.0     0.0        0.0      0        2   \n",
+       "3      0.0     0.0           0.0     0.0        0.0      1        2   \n",
+       "4      0.0     0.0           1.0     0.0        0.0      1        1   \n",
+       "\n",
+       "   birthcontrol  pregnancies  cholesterol  hightax82   price71   price82  \\\n",
+       "0           2.0          NaN        197.0        0.0  2.183594  1.739990   \n",
+       "1           2.0          NaN        301.0        0.0  2.346680  1.797363   \n",
+       "2           0.0          2.0        157.0        0.0  1.569580  1.513428   \n",
+       "3           2.0          NaN        174.0        0.0  1.506592  1.451904   \n",
+       "4           2.0          NaN        216.0        0.0  2.346680  1.797363   \n",
+       "\n",
+       "      tax71     tax82  price71_82  tax71_82  id  censored  older  \n",
+       "0  1.102295  0.461975    0.443787  0.640381   1       0.0    0.0  \n",
+       "1  1.364990  0.571899    0.549316  0.792969   2       0.0    0.0  \n",
+       "2  0.551270  0.230988    0.056198  0.320251   3       0.0    1.0  \n",
+       "3  0.524902  0.219971    0.054794  0.304993   4       0.0    1.0  \n",
+       "4  1.364990  0.571899    0.549316  0.792969   5       0.0    0.0  "
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = nhefs.load_pandas().data.copy()\n",
+    "\n",
+    "print(\"Shape of Data:\")\n",
+    "print(df.shape)\n",
+    "\n",
+    "print(\"Variable Names:\")\n",
+    "print(df.columns)\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "915637df",
+   "metadata": {},
+   "source": [
+    "- **처치변수**: `qsmk` — 1982년 추적조사 시 금연 여부 (1=금연, 0=계속 흡연)  \n",
+    "- **결과변수**: `wt82_71` — 체중 변화량(kg) = 1982년 체중 – 1971년 체중  \n",
+    "- **통제변수**:  \n",
+    "  - `sex`: 성별 (0=남성, 1=여성)  \n",
+    "  - `age`: baseline 만 나이  \n",
+    "  - `race`: 인종 (0=백인, 1=기타)  \n",
+    "  - `education`: 학력 수준 (5단계 범주)  \n",
+    "  - `smokeintensity`: baseline 하루 평균 흡연량(개비)  \n",
+    "  - `smokeyrs`: 흡연 지속 기간(년)  \n",
+    "  - `active`: 일상생활 활동 수준 (3단계)  \n",
+    "  - `exercise`: 여가시간 운동 수준 (3단계)  \n",
+    "  - `wt71`: baseline 체중(kg)  \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "ff2f4b4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "처치군/대조군:\n",
+      " qsmk\n",
+      "0.0    1163\n",
+      "1.0     403\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "treatment = \"qsmk\"\n",
+    "outcome = \"wt82_71\"\n",
+    "\n",
+    "confounders = [\n",
+    "    \"sex\",\n",
+    "    \"age\",              \n",
+    "    \"race\",             \n",
+    "    \"education\",        \n",
+    "    \"smokeintensity\",   \n",
+    "    \"smokeyrs\",         \n",
+    "    \"active\",           \n",
+    "    \"exercise\",         \n",
+    "    \"wt71\"              \n",
+    "]\n",
+    "\n",
+    "vars_needed = [treatment, outcome] + confounders\n",
+    "df_clean = df[vars_needed].dropna()\n",
+    "\n",
+    "print(\"처치군/대조군:\\n\", df_clean[treatment].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "d985c724",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 범주형 변수 원핫 인코딩\n",
+    "categorical_vars = [\"sex\", \"race\", \"education\", \"active\", \"exercise\"]\n",
+    "df_encoded = pd.get_dummies(df_clean, columns=categorical_vars, drop_first=True)\n",
+    "\n",
+    "# 연속형 변수 스케일링\n",
+    "numeric_confounders = [\"age\", \"smokeintensity\", \"smokeyrs\", \"wt71\"]\n",
+    "scaler = StandardScaler()\n",
+    "df_encoded[numeric_confounders] = scaler.fit_transform(df_encoded[numeric_confounders])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25ebfea3",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "65df40db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 시각화용 DAG (원핫 인코딩 전 변수 기준)\n",
+    "gml_graph_viz = \"\"\"\n",
+    "graph [\n",
+    "  directed 1\n",
+    "\n",
+    "  node [ id \"qsmk\" label \"qsmk\" ]\n",
+    "  node [ id \"wt82_71\" label \"wt82_71\" ]\n",
+    "\n",
+    "  node [ id \"sex\" label \"sex\" ]\n",
+    "  node [ id \"age\" label \"age\" ]\n",
+    "  node [ id \"race\" label \"race\" ]\n",
+    "  node [ id \"education\" label \"education\" ]\n",
+    "  node [ id \"smokeintensity\" label \"smokeintensity\" ]\n",
+    "  node [ id \"smokeyrs\" label \"smokeyrs\" ]\n",
+    "  node [ id \"active\" label \"active\" ]\n",
+    "  node [ id \"exercise\" label \"exercise\" ]\n",
+    "  node [ id \"wt71\" label \"wt71\" ]\n",
+    "\n",
+    "  edge [ source \"sex\" target \"qsmk\" ]\n",
+    "  edge [ source \"sex\" target \"wt82_71\" ]\n",
+    "  edge [ source \"age\" target \"qsmk\" ]\n",
+    "  edge [ source \"age\" target \"wt82_71\" ]\n",
+    "  edge [ source \"race\" target \"qsmk\" ]\n",
+    "  edge [ source \"race\" target \"wt82_71\" ]\n",
+    "  edge [ source \"education\" target \"qsmk\" ]\n",
+    "  edge [ source \"education\" target \"wt82_71\" ]\n",
+    "  edge [ source \"smokeintensity\" target \"qsmk\" ]\n",
+    "  edge [ source \"smokeintensity\" target \"wt82_71\" ]\n",
+    "  edge [ source \"smokeyrs\" target \"qsmk\" ]\n",
+    "  edge [ source \"smokeyrs\" target \"wt82_71\" ]\n",
+    "  edge [ source \"active\" target \"qsmk\" ]\n",
+    "  edge [ source \"active\" target \"wt82_71\" ]\n",
+    "  edge [ source \"exercise\" target \"qsmk\" ]\n",
+    "  edge [ source \"exercise\" target \"wt82_71\" ]\n",
+    "  edge [ source \"wt71\" target \"qsmk\" ]\n",
+    "  edge [ source \"wt71\" target \"wt82_71\" ]\n",
+    "\n",
+    "  edge [ source \"qsmk\" target \"wt82_71\" ]\n",
+    "]\n",
+    "\"\"\"\n",
+    "\n",
+    "cm_for_viz = CausalModel(data=df_clean, treatment=treatment, outcome=outcome, graph=gml_graph_viz)\n",
+    "cm_for_viz.view_model(layout=\"dot\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "2539d612",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 추정용 DAG\n",
+    "#   - 모든 공변량(원핫 포함) -> {qsmk, wt82_71}\n",
+    "#   - qsmk -> wt82_71\n",
+    "\n",
+    "all_cols = df_encoded.columns.tolist()\n",
+    "confounder_cols = [c for c in all_cols if c not in [treatment, outcome]]\n",
+    "\n",
+    "gml_nodes = []\n",
+    "gml_nodes.append(f'  node [ id \"{treatment}\" label \"{treatment}\" ]')\n",
+    "gml_nodes.append(f'  node [ id \"{outcome}\" label \"{outcome}\" ]')\n",
+    "for c in confounder_cols:\n",
+    "    gml_nodes.append(f'  node [ id \"{c}\" label \"{c}\" ]')\n",
+    "\n",
+    "gml_edges = []\n",
+    "for c in confounder_cols:\n",
+    "    gml_edges.append(f'  edge [ source \"{c}\" target \"{treatment}\" ]')\n",
+    "    gml_edges.append(f'  edge [ source \"{c}\" target \"{outcome}\" ]')\n",
+    "gml_edges.append(f'  edge [ source \"{treatment}\" target \"{outcome}\" ]')\n",
+    "\n",
+    "gml_graph = 'graph [\\n  directed 1\\n\\n' + \"\\n\".join(gml_nodes) + \"\\n\\n\" + \"\\n\".join(gml_edges) + \"\\n]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5aa85b75",
+   "metadata": {},
+   "source": [
+    "## Identify"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "5d714d62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
+      "\n",
+      "### Estimand : 1\n",
+      "Estimand name: backdoor\n",
+      "Estimand expression:\n",
+      "   d                                                                           ↪\n",
+      "───────(E[wt_82_71|exercise_1,education_4,exercise_2,active_1,age,education_5, ↪\n",
+      "d[qsmk]                                                                        ↪\n",
+      "\n",
+      "↪                                                                             \n",
+      "↪ race_1,education_3,active_2,smokeintensity,sex_1,smokeyrs,education_2,wt71])\n",
+      "↪                                                                             \n",
+      "Estimand assumption 1, Unconfoundedness: If U→{qsmk} and U→wt82_71 then P(wt82_71|qsmk,exercise_1,education_4,exercise_2,active_1,age,education_5,race_1,education_3,active_2,smokeintensity,sex_1,smokeyrs,education_2,wt71,U) = P(wt82_71|qsmk,exercise_1,education_4,exercise_2,active_1,age,education_5,race_1,education_3,active_2,smokeintensity,sex_1,smokeyrs,education_2,wt71)\n",
+      "\n",
+      "### Estimand : 2\n",
+      "Estimand name: iv\n",
+      "No such variable(s) found!\n",
+      "\n",
+      "### Estimand : 3\n",
+      "Estimand name: frontdoor\n",
+      "No such variable(s) found!\n",
+      "\n",
+      "### Estimand : 4\n",
+      "Estimand name: general_adjustment\n",
+      "Estimand expression:\n",
+      "   d                                                                           ↪\n",
+      "───────(E[wt_82_71|education_4,exercise_2,education_2,active_1,age,education_5 ↪\n",
+      "d[qsmk]                                                                        ↪\n",
+      "\n",
+      "↪                                                                             \n",
+      "↪ ,race_1,smokeintensity,education_3,active_2,exercise_1,sex_1,smokeyrs,wt71])\n",
+      "↪                                                                             \n",
+      "Estimand assumption 1, Unconfoundedness: If U→{qsmk} and U→wt82_71 then P(wt82_71|qsmk,education_4,exercise_2,education_2,active_1,age,education_5,race_1,smokeintensity,education_3,active_2,exercise_1,sex_1,smokeyrs,wt71,U) = P(wt82_71|qsmk,education_4,exercise_2,education_2,active_1,age,education_5,race_1,smokeintensity,education_3,active_2,exercise_1,sex_1,smokeyrs,wt71)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "est_model = CausalModel(\n",
+    "    data=df_encoded,\n",
+    "    treatment=treatment,\n",
+    "    outcome=outcome,\n",
+    "    graph=gml_graph\n",
+    ")\n",
+    "\n",
+    "estimand = est_model.identify_effect()\n",
+    "print(estimand)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea10922e",
+   "metadata": {},
+   "source": [
+    "- **Backdoor adjustment 가능**  \n",
+    "  모든 공변량을 조건화하면 `qsmk → wt82_71` 인과효과를 식별할 수 있습니다.\n",
+    "\n",
+    "- **IV, Front-door 전략 없음**  \n",
+    "  도구변수(Instrument)나 매개변수(Mediator)로 활용할 만한 변수가 존재하지 않습니다.\n",
+    "\n",
+    "- **가정 (Unconfoundedness)**  \n",
+    "  관측되지 않은 잠재적 교란 변수(U)가 `qsmk`와 `wt82_71` 모두에 동시에 영향을 주지 않는다고 가정해야 합니다.\n",
+    "\n",
+    "따라서 **Back-door Adjustment**를 통해 금연(`qsmk`)이 체중 변화(`wt82_71`)에 미치는 ATE를 추정할 수 있습니다."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9c024ad",
+   "metadata": {},
+   "source": [
+    "## Estimate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e662236a",
+   "metadata": {},
+   "source": [
+    "### 1. Linear Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "830fe396",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[ATE] Linear Regression: 3.381171033988095\n"
+     ]
+    }
+   ],
+   "source": [
+    "estimate_lr = est_model.estimate_effect(\n",
+    "    identified_estimand=estimand,\n",
+    "    method_name=\"backdoor.linear_regression\"\n",
+    ")\n",
+    "print(\"\\n[ATE] Linear Regression:\", estimate_lr.value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e6509e2",
+   "metadata": {},
+   "source": [
+    "### 2. Doubly Robust Learner"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "a7aa0ee5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ATE] DR Learner: 3.738829410323152\n"
+     ]
+    }
+   ],
+   "source": [
+    "estimate_drl = est_model.estimate_effect(\n",
+    "    identified_estimand=estimand,\n",
+    "    method_name=\"backdoor.econml.dr.DRLearner\",\n",
+    "    method_params={\n",
+    "        \"init_params\": {\n",
+    "            \"model_propensity\": LogisticRegression(max_iter=5000),\n",
+    "            \"model_regression\": GradientBoostingRegressor(random_state=42)\n",
+    "        },\n",
+    "        \"fit_params\": {}\n",
+    "    }\n",
+    ")\n",
+    "print(\"[ATE] DR Learner:\", estimate_drl.value)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43b9462a",
+   "metadata": {},
+   "source": [
+    "### 3. Double Machine Learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "a91cdd48",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ATE] DML: 3.9002505426347462\n",
+      "[ATE] DML 95% CI: [[[3.19674121]]\n",
+      "\n",
+      " [[5.20465502]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "estimate_dml = est_model.estimate_effect(\n",
+    "    identified_estimand=estimand,\n",
+    "    method_name=\"backdoor.econml.dml.DML\",\n",
+    "    control_value=0,\n",
+    "    treatment_value=1,\n",
+    "    target_units=\"ate\",\n",
+    "    confidence_intervals=True,\n",
+    "    method_params={\n",
+    "        \"init_params\": {\n",
+    "            \"model_y\": GradientBoostingRegressor(random_state=42),\n",
+    "            \"model_t\": GradientBoostingRegressor(random_state=42),\n",
+    "            \"model_final\": LinearRegression(fit_intercept=False),\n",
+    "        },\n",
+    "        \"fit_params\": {\n",
+    "            \"inference\": BootstrapInference(n_bootstrap_samples=500, n_jobs=-1)\n",
+    "        }\n",
+    "    }\n",
+    ")\n",
+    "print(\"[ATE] DML:\", estimate_dml.value)\n",
+    "print(\"[ATE] DML 95% CI:\", estimate_dml.get_confidence_intervals())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "id": "80620f5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ATE] DML: 3.5041273735125875\n"
+     ]
+    }
+   ],
+   "source": [
+    "estimate_dml = est_model.estimate_effect(\n",
+    "    identified_estimand=estimand,\n",
+    "    method_name=\"backdoor.econml.dml.DML\",\n",
+    "    control_value=0,\n",
+    "    treatment_value=1,\n",
+    "    target_units=\"ate\",\n",
+    "    method_params={\n",
+    "        \"init_params\": {\n",
+    "            \"model_y\": 'auto',\n",
+    "            \"model_t\": 'auto',\n",
+    "            \"model_final\": LinearRegression(fit_intercept=False)\n",
+    "        },\n",
+    "        \"fit_params\": {}\n",
+    "    }\n",
+    ")\n",
+    "print(\"[ATE] DML:\", estimate_dml.value)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/book/scm/overview.md b/book/scm/overview.md
new file mode 100644
index 0000000..1a96adb
--- /dev/null
+++ b/book/scm/overview.md
@@ -0,0 +1,9 @@
+# SCM
+구조적 인과 모형(SCM)은 design-based approach와 달리, 인과관계를 DAG로 모델링하여 식별 가정을 명시적으로 표현하는 접근법입니다.
+
+SCM을 통한 인과추론 과정은 다음과 같이 요약됩니다:
+
+1. **Model**: 변수들 사이의 인과적 관계를 DAG로 정의하고, 혼란변수(confounder), 매개변수(mediator), 충돌변수(collider) 등을 구분합니다.
+2. **Identify**: 관심 있는 인과효과가 그래프에서 식별 가능한지 검토하고, Back-door, Front-door, 도구변수(IV)와 같은 식별 전략을 탐색합니다.
+3. **Estimate**: 식별 전략에 적합한 통계적 방법으로 인과효과를 추정합니다.
+4. **Refute**: 위약(placebo) 분석, 무작위 교란(Randomization Test) 등을 통해 추정된 인과효과의 강건성을 검증합니다.
\ No newline at end of file

From 9edda848defc0d66c61d2e44035ad8da28e6ef96 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=EC=A1=B0=ED=95=B4=EC=B0=BD?=
 <hc.cho@johaechang-ui-MacBookPro-2.local>
Date: Fri, 3 Oct 2025 17:05:12 +0900
Subject: [PATCH 2/6] update toc.yml

---
 book/_toc.yml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/book/_toc.yml b/book/_toc.yml
index ccc6cd6..76c8cb7 100644
--- a/book/_toc.yml
+++ b/book/_toc.yml
@@ -5,6 +5,9 @@ parts:
   - caption: Contents
     chapters:
     - file: prior_knowledge/overview.md
+    - file: scm/overview.md
+      sections:
+      - file: scm/graph_based_approach.ipynb
     - file: ate/overview.md
       sections:
       - file: ate/ols.ipynb

From f6e67c4ad62224cf1e8771c22ed7361bbbc18033 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=EC=A1=B0=ED=95=B4=EC=B0=BD?=
 <hc.cho@johaechang-ui-MacBookPro-2.local>
Date: Fri, 3 Oct 2025 17:06:04 +0900
Subject: [PATCH 3/6] delete dag.png

---
 book/scm/causal_model.png | Bin 83821 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 book/scm/causal_model.png

diff --git a/book/scm/causal_model.png b/book/scm/causal_model.png
deleted file mode 100644
index ab99255db477b472d5eff520a1a9723ca2e6d20d..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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From 121837c93e29975cac8c6b1a06c1548df43beac6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=EC=A1=B0=ED=95=B4=EC=B0=BD?=
 <hc.cho@johaechang-ui-MacBookPro-2.local>
Date: Fri, 3 Oct 2025 23:48:24 +0900
Subject: [PATCH 4/6] update scm

---
 book/scm/graph_based_approach.ipynb | 33 -----------------------------
 1 file changed, 33 deletions(-)

diff --git a/book/scm/graph_based_approach.ipynb b/book/scm/graph_based_approach.ipynb
index ffe2870..c06157e 100644
--- a/book/scm/graph_based_approach.ipynb
+++ b/book/scm/graph_based_approach.ipynb
@@ -1046,39 +1046,6 @@
     "print(\"[ATE] DML 95% CI:\", estimate_dml.get_confidence_intervals())\n",
     "\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "id": "80620f5d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ATE] DML: 3.5041273735125875\n"
-     ]
-    }
-   ],
-   "source": [
-    "estimate_dml = est_model.estimate_effect(\n",
-    "    identified_estimand=estimand,\n",
-    "    method_name=\"backdoor.econml.dml.DML\",\n",
-    "    control_value=0,\n",
-    "    treatment_value=1,\n",
-    "    target_units=\"ate\",\n",
-    "    method_params={\n",
-    "        \"init_params\": {\n",
-    "            \"model_y\": 'auto',\n",
-    "            \"model_t\": 'auto',\n",
-    "            \"model_final\": LinearRegression(fit_intercept=False)\n",
-    "        },\n",
-    "        \"fit_params\": {}\n",
-    "    }\n",
-    ")\n",
-    "print(\"[ATE] DML:\", estimate_dml.value)"
-   ]
   }
  ],
  "metadata": {

From 0458ef708d7787d04246e2bd77c3b59648f47ea9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=EC=A1=B0=ED=95=B4=EC=B0=BD?=
 <hc.cho@johaechang-ui-MacBookPro-2.local>
Date: Sat, 11 Oct 2025 17:22:10 +0900
Subject: [PATCH 5/6] update backdoor criterion

---
 book/_toc.yml                                 |   2 +-
 ...pproach.ipynb => backdoor_criterion.ipynb} | 717 +++++++-----------
 book/scm/overview.md                          |   2 +-
 3 files changed, 290 insertions(+), 431 deletions(-)
 rename book/scm/{graph_based_approach.ipynb => backdoor_criterion.ipynb} (84%)

diff --git a/book/_toc.yml b/book/_toc.yml
index 76c8cb7..a0ac836 100644
--- a/book/_toc.yml
+++ b/book/_toc.yml
@@ -7,7 +7,7 @@ parts:
     - file: prior_knowledge/overview.md
     - file: scm/overview.md
       sections:
-      - file: scm/graph_based_approach.ipynb
+      - file: scm/backdoor_criterion.ipynb
     - file: ate/overview.md
       sections:
       - file: ate/ols.ipynb
diff --git a/book/scm/graph_based_approach.ipynb b/book/scm/backdoor_criterion.ipynb
similarity index 84%
rename from book/scm/graph_based_approach.ipynb
rename to book/scm/backdoor_criterion.ipynb
index c06157e..e8a2731 100644
--- a/book/scm/graph_based_approach.ipynb
+++ b/book/scm/backdoor_criterion.ipynb
@@ -5,7 +5,7 @@
    "id": "de707838",
    "metadata": {},
    "source": [
-    "# Graph based approach"
+    "# Backdoor Criterion"
    ]
   },
   {
@@ -18,71 +18,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
+   "id": "301b8d0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "%pip install dowhy==0.13 causaldata econml scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "id": "dd8821be",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: dowhy==0.13 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (0.13)\n",
-      "Requirement already satisfied: causaldata in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (0.1.5)\n",
-      "Requirement already satisfied: econml in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (0.16.0)\n",
-      "Requirement already satisfied: scikit-learn in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (1.6.1)\n",
-      "Requirement already satisfied: causal-learn>=0.1.3.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (0.1.4.3)\n",
-      "Requirement already satisfied: cvxpy<1.5 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (1.4.4)\n",
-      "Requirement already satisfied: cython>=3.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (3.1.4)\n",
-      "Requirement already satisfied: joblib>=1.1.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (1.5.2)\n",
-      "Requirement already satisfied: networkx>=3.3 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (3.5)\n",
-      "Requirement already satisfied: numba>=0.59 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (0.60.0)\n",
-      "Requirement already satisfied: numpy>1.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (2.0.2)\n",
-      "Requirement already satisfied: pandas>1.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (2.2.2)\n",
-      "Requirement already satisfied: scipy<=1.15.3 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (1.15.3)\n",
-      "Requirement already satisfied: statsmodels>=0.14 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (0.14.5)\n",
-      "Requirement already satisfied: sympy>=1.10.1 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (1.14.0)\n",
-      "Requirement already satisfied: tqdm>=4.64.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from dowhy==0.13) (4.67.1)\n",
-      "Requirement already satisfied: osqp>=0.6.2 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from cvxpy<1.5->dowhy==0.13) (1.0.4)\n",
-      "Requirement already satisfied: ecos>=2 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from cvxpy<1.5->dowhy==0.13) (2.0.14)\n",
-      "Requirement already satisfied: clarabel>=0.5.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from cvxpy<1.5->dowhy==0.13) (0.11.1)\n",
-      "Requirement already satisfied: scs>=3.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from cvxpy<1.5->dowhy==0.13) (3.2.8)\n",
-      "Requirement already satisfied: pybind11 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from cvxpy<1.5->dowhy==0.13) (3.0.1)\n",
-      "Requirement already satisfied: sparse in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from econml) (0.17.0)\n",
-      "Requirement already satisfied: shap<0.49.0,>=0.38.1 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from econml) (0.48.0)\n",
-      "Requirement already satisfied: lightgbm in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from econml) (4.6.0)\n",
-      "Requirement already satisfied: packaging in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from econml) (25.0)\n",
-      "Requirement already satisfied: threadpoolctl>=3.1.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from scikit-learn) (3.6.0)\n",
-      "Requirement already satisfied: slicer==0.0.8 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from shap<0.49.0,>=0.38.1->econml) (0.0.8)\n",
-      "Requirement already satisfied: cloudpickle in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from shap<0.49.0,>=0.38.1->econml) (3.1.1)\n",
-      "Requirement already satisfied: typing-extensions in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from shap<0.49.0,>=0.38.1->econml) (4.15.0)\n",
-      "Requirement already satisfied: graphviz in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from causal-learn>=0.1.3.0->dowhy==0.13) (0.21)\n",
-      "Requirement already satisfied: matplotlib in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from causal-learn>=0.1.3.0->dowhy==0.13) (3.10.6)\n",
-      "Requirement already satisfied: pydot in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from causal-learn>=0.1.3.0->dowhy==0.13) (4.0.1)\n",
-      "Requirement already satisfied: momentchi2 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from causal-learn>=0.1.3.0->dowhy==0.13) (0.1.8)\n",
-      "Requirement already satisfied: cffi in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from clarabel>=0.5.0->cvxpy<1.5->dowhy==0.13) (2.0.0)\n",
-      "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from numba>=0.59->dowhy==0.13) (0.43.0)\n",
-      "Requirement already satisfied: jinja2 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from osqp>=0.6.2->cvxpy<1.5->dowhy==0.13) (3.1.6)\n",
-      "Requirement already satisfied: setuptools in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from osqp>=0.6.2->cvxpy<1.5->dowhy==0.13) (80.9.0)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from pandas>1.0->dowhy==0.13) (2.9.0.post0)\n",
-      "Requirement already satisfied: pytz>=2020.1 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from pandas>1.0->dowhy==0.13) (2025.2)\n",
-      "Requirement already satisfied: tzdata>=2022.7 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from pandas>1.0->dowhy==0.13) (2025.2)\n",
-      "Requirement already satisfied: six>=1.5 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas>1.0->dowhy==0.13) (1.17.0)\n",
-      "Requirement already satisfied: patsy>=0.5.6 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from statsmodels>=0.14->dowhy==0.13) (1.0.1)\n",
-      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from sympy>=1.10.1->dowhy==0.13) (1.3.0)\n",
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-      "Requirement already satisfied: contourpy>=1.0.1 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from matplotlib->causal-learn>=0.1.3.0->dowhy==0.13) (1.3.3)\n",
-      "Requirement already satisfied: cycler>=0.10 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from matplotlib->causal-learn>=0.1.3.0->dowhy==0.13) (0.12.1)\n",
-      "Requirement already satisfied: fonttools>=4.22.0 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from matplotlib->causal-learn>=0.1.3.0->dowhy==0.13) (4.60.0)\n",
-      "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from matplotlib->causal-learn>=0.1.3.0->dowhy==0.13) (1.4.9)\n",
-      "Requirement already satisfied: pillow>=8 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from matplotlib->causal-learn>=0.1.3.0->dowhy==0.13) (11.3.0)\n",
-      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.12/site-packages (from matplotlib->causal-learn>=0.1.3.0->dowhy==0.13) (3.2.4)\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%pip install dowhy==0.13 causaldata econml scikit-learn\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
     "\n",
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -93,7 +46,7 @@
     "from sklearn.ensemble import GradientBoostingRegressor\n",
     "\n",
     "from dowhy import CausalModel\n",
-    "from econml.inference import BootstrapInference\n"
+    "from econml.inference import BootstrapInference"
    ]
   },
   {
@@ -109,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 3,
    "id": "3f63bbe8",
    "metadata": {},
    "outputs": [
@@ -165,53 +118,7 @@
        "      <th>dbp</th>\n",
        "      <th>sex</th>\n",
        "      <th>age</th>\n",
-       "      <th>race</th>\n",
-       "      <th>income</th>\n",
-       "      <th>marital</th>\n",
-       "      <th>school</th>\n",
-       "      <th>education</th>\n",
-       "      <th>ht</th>\n",
-       "      <th>wt71</th>\n",
-       "      <th>wt82</th>\n",
-       "      <th>wt82_71</th>\n",
-       "      <th>birthplace</th>\n",
-       "      <th>smokeintensity</th>\n",
-       "      <th>smkintensity82_71</th>\n",
-       "      <th>smokeyrs</th>\n",
-       "      <th>asthma</th>\n",
-       "      <th>bronch</th>\n",
-       "      <th>tb</th>\n",
-       "      <th>hf</th>\n",
-       "      <th>hbp</th>\n",
-       "      <th>pepticulcer</th>\n",
-       "      <th>colitis</th>\n",
-       "      <th>hepatitis</th>\n",
-       "      <th>chroniccough</th>\n",
-       "      <th>hayfever</th>\n",
-       "      <th>diabetes</th>\n",
-       "      <th>polio</th>\n",
-       "      <th>tumor</th>\n",
-       "      <th>nervousbreak</th>\n",
-       "      <th>alcoholpy</th>\n",
-       "      <th>alcoholfreq</th>\n",
-       "      <th>alcoholtype</th>\n",
-       "      <th>alcoholhowmuch</th>\n",
-       "      <th>pica</th>\n",
-       "      <th>headache</th>\n",
-       "      <th>otherpain</th>\n",
-       "      <th>weakheart</th>\n",
-       "      <th>allergies</th>\n",
-       "      <th>nerves</th>\n",
-       "      <th>lackpep</th>\n",
-       "      <th>hbpmed</th>\n",
-       "      <th>boweltrouble</th>\n",
-       "      <th>wtloss</th>\n",
-       "      <th>infection</th>\n",
-       "      <th>active</th>\n",
-       "      <th>exercise</th>\n",
-       "      <th>birthcontrol</th>\n",
-       "      <th>pregnancies</th>\n",
-       "      <th>cholesterol</th>\n",
+       "      <th>...</th>\n",
        "      <th>hightax82</th>\n",
        "      <th>price71</th>\n",
        "      <th>price82</th>\n",
@@ -237,53 +144,7 @@
        "      <td>96.0</td>\n",
        "      <td>0</td>\n",
        "      <td>42.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>174.1875</td>\n",
-       "      <td>79.04</td>\n",
-       "      <td>68.946040</td>\n",
-       "      <td>-10.093960</td>\n",
-       "      <td>47.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>197.0</td>\n",
+       "      <td>...</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2.183594</td>\n",
        "      <td>1.739990</td>\n",
@@ -307,53 +168,7 @@
        "      <td>80.0</td>\n",
        "      <td>0</td>\n",
        "      <td>36.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>159.3750</td>\n",
-       "      <td>58.63</td>\n",
-       "      <td>61.234970</td>\n",
-       "      <td>2.604970</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>-10.0</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>301.0</td>\n",
+       "      <td>...</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2.346680</td>\n",
        "      <td>1.797363</td>\n",
@@ -377,53 +192,7 @@
        "      <td>75.0</td>\n",
        "      <td>1</td>\n",
        "      <td>56.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>168.5000</td>\n",
-       "      <td>56.81</td>\n",
-       "      <td>66.224486</td>\n",
-       "      <td>9.414486</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>-14.0</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>157.0</td>\n",
+       "      <td>...</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.569580</td>\n",
        "      <td>1.513428</td>\n",
@@ -447,53 +216,7 @@
        "      <td>78.0</td>\n",
        "      <td>0</td>\n",
        "      <td>68.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>170.1875</td>\n",
-       "      <td>59.42</td>\n",
-       "      <td>64.410117</td>\n",
-       "      <td>4.990117</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>53.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>174.0</td>\n",
+       "      <td>...</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.506592</td>\n",
        "      <td>1.451904</td>\n",
@@ -517,53 +240,7 @@
        "      <td>77.0</td>\n",
        "      <td>0</td>\n",
        "      <td>40.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>181.8750</td>\n",
-       "      <td>87.09</td>\n",
-       "      <td>92.079251</td>\n",
-       "      <td>4.989251</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>216.0</td>\n",
+       "      <td>...</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2.346680</td>\n",
        "      <td>1.797363</td>\n",
@@ -577,74 +254,35 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>5 rows × 67 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "    seqn  qsmk  death  yrdth  modth  dadth    sbp   dbp sex   age race  \\\n",
-       "0  233.0   0.0    0.0    NaN    NaN    NaN  175.0  96.0   0  42.0    1   \n",
-       "1  235.0   0.0    0.0    NaN    NaN    NaN  123.0  80.0   0  36.0    0   \n",
-       "2  244.0   0.0    0.0    NaN    NaN    NaN  115.0  75.0   1  56.0    1   \n",
-       "3  245.0   0.0    1.0   85.0    2.0   14.0  148.0  78.0   0  68.0    1   \n",
-       "4  252.0   0.0    0.0    NaN    NaN    NaN  118.0  77.0   0  40.0    0   \n",
-       "\n",
-       "   income  marital  school education        ht   wt71       wt82    wt82_71  \\\n",
-       "0    19.0      2.0     7.0         1  174.1875  79.04  68.946040 -10.093960   \n",
-       "1    18.0      2.0     9.0         2  159.3750  58.63  61.234970   2.604970   \n",
-       "2    15.0      3.0    11.0         2  168.5000  56.81  66.224486   9.414486   \n",
-       "3    15.0      3.0     5.0         1  170.1875  59.42  64.410117   4.990117   \n",
-       "4    18.0      2.0    11.0         2  181.8750  87.09  92.079251   4.989251   \n",
-       "\n",
-       "   birthplace  smokeintensity  smkintensity82_71  smokeyrs  asthma  bronch  \\\n",
-       "0        47.0            30.0              -10.0      29.0     0.0     0.0   \n",
-       "1        42.0            20.0              -10.0      24.0     0.0     0.0   \n",
-       "2        51.0            20.0              -14.0      26.0     0.0     0.0   \n",
-       "3        37.0             3.0                4.0      53.0     0.0     0.0   \n",
-       "4        42.0            20.0                0.0      19.0     0.0     0.0   \n",
-       "\n",
-       "    tb   hf  hbp  pepticulcer  colitis  hepatitis  chroniccough  hayfever  \\\n",
-       "0  0.0  0.0  1.0          1.0      0.0        0.0           0.0       0.0   \n",
-       "1  0.0  0.0  0.0          0.0      0.0        0.0           0.0       0.0   \n",
-       "2  0.0  0.0  0.0          0.0      0.0        0.0           0.0       1.0   \n",
-       "3  0.0  0.0  1.0          0.0      0.0        0.0           0.0       0.0   \n",
-       "4  0.0  0.0  0.0          0.0      0.0        0.0           0.0       0.0   \n",
+       "    seqn  qsmk  death  yrdth  modth  dadth    sbp   dbp sex   age  ...  \\\n",
+       "0  233.0   0.0    0.0    NaN    NaN    NaN  175.0  96.0   0  42.0  ...   \n",
+       "1  235.0   0.0    0.0    NaN    NaN    NaN  123.0  80.0   0  36.0  ...   \n",
+       "2  244.0   0.0    0.0    NaN    NaN    NaN  115.0  75.0   1  56.0  ...   \n",
+       "3  245.0   0.0    1.0   85.0    2.0   14.0  148.0  78.0   0  68.0  ...   \n",
+       "4  252.0   0.0    0.0    NaN    NaN    NaN  118.0  77.0   0  40.0  ...   \n",
        "\n",
-       "   diabetes  polio  tumor  nervousbreak  alcoholpy  alcoholfreq  alcoholtype  \\\n",
-       "0       1.0    0.0    0.0           0.0        1.0          1.0          3.0   \n",
-       "1       0.0    0.0    0.0           0.0        1.0          0.0          1.0   \n",
-       "2       0.0    0.0    1.0           0.0        1.0          3.0          4.0   \n",
-       "3       0.0    0.0    0.0           0.0        1.0          2.0          3.0   \n",
-       "4       0.0    0.0    0.0           0.0        1.0          2.0          1.0   \n",
+       "  hightax82   price71   price82     tax71     tax82  price71_82  tax71_82  id  \\\n",
+       "0       0.0  2.183594  1.739990  1.102295  0.461975    0.443787  0.640381   1   \n",
+       "1       0.0  2.346680  1.797363  1.364990  0.571899    0.549316  0.792969   2   \n",
+       "2       0.0  1.569580  1.513428  0.551270  0.230988    0.056198  0.320251   3   \n",
+       "3       0.0  1.506592  1.451904  0.524902  0.219971    0.054794  0.304993   4   \n",
+       "4       0.0  2.346680  1.797363  1.364990  0.571899    0.549316  0.792969   5   \n",
        "\n",
-       "   alcoholhowmuch  pica  headache  otherpain  weakheart  allergies  nerves  \\\n",
-       "0             7.0   0.0       1.0        0.0        0.0        0.0     0.0   \n",
-       "1             4.0   0.0       1.0        0.0        0.0        0.0     0.0   \n",
-       "2             NaN   0.0       1.0        1.0        0.0        0.0     1.0   \n",
-       "3             4.0   0.0       0.0        1.0        1.0        0.0     0.0   \n",
-       "4             2.0   0.0       1.0        0.0        0.0        0.0     0.0   \n",
+       "   censored  older  \n",
+       "0       0.0    0.0  \n",
+       "1       0.0    0.0  \n",
+       "2       0.0    1.0  \n",
+       "3       0.0    1.0  \n",
+       "4       0.0    0.0  \n",
        "\n",
-       "   lackpep  hbpmed  boweltrouble  wtloss  infection active exercise  \\\n",
-       "0      0.0     1.0           0.0     0.0        0.0      0        2   \n",
-       "1      0.0     0.0           0.0     0.0        1.0      0        0   \n",
-       "2      0.0     0.0           0.0     0.0        0.0      0        2   \n",
-       "3      0.0     0.0           0.0     0.0        0.0      1        2   \n",
-       "4      0.0     0.0           1.0     0.0        0.0      1        1   \n",
-       "\n",
-       "   birthcontrol  pregnancies  cholesterol  hightax82   price71   price82  \\\n",
-       "0           2.0          NaN        197.0        0.0  2.183594  1.739990   \n",
-       "1           2.0          NaN        301.0        0.0  2.346680  1.797363   \n",
-       "2           0.0          2.0        157.0        0.0  1.569580  1.513428   \n",
-       "3           2.0          NaN        174.0        0.0  1.506592  1.451904   \n",
-       "4           2.0          NaN        216.0        0.0  2.346680  1.797363   \n",
-       "\n",
-       "      tax71     tax82  price71_82  tax71_82  id  censored  older  \n",
-       "0  1.102295  0.461975    0.443787  0.640381   1       0.0    0.0  \n",
-       "1  1.364990  0.571899    0.549316  0.792969   2       0.0    0.0  \n",
-       "2  0.551270  0.230988    0.056198  0.320251   3       0.0    1.0  \n",
-       "3  0.524902  0.219971    0.054794  0.304993   4       0.0    1.0  \n",
-       "4  1.364990  0.571899    0.549316  0.792969   5       0.0    0.0  "
+       "[5 rows x 67 columns]"
       ]
      },
-     "execution_count": 139,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -682,7 +320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 4,
    "id": "ff2f4b4b",
    "metadata": {},
    "outputs": [
@@ -722,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 5,
    "id": "d985c724",
    "metadata": {},
    "outputs": [],
@@ -747,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 21,
    "id": "65df40db",
    "metadata": {},
    "outputs": [
@@ -805,12 +443,14 @@
     "\"\"\"\n",
     "\n",
     "cm_for_viz = CausalModel(data=df_clean, treatment=treatment, outcome=outcome, graph=gml_graph_viz)\n",
-    "cm_for_viz.view_model(layout=\"dot\")"
+    "cm_for_viz.view_model(\n",
+    "    layout=\"dot\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 7,
    "id": "2539d612",
    "metadata": {},
    "outputs": [],
@@ -847,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 8,
    "id": "5d714d62",
    "metadata": {},
    "outputs": [
@@ -861,13 +501,13 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "   d                                                                           ↪\n",
-      "───────(E[wt_82_71|exercise_1,education_4,exercise_2,active_1,age,education_5, ↪\n",
+      "───────(E[wt_82_71|sex_1,active_1,education_4,race_1,education_2,wt71,educatio ↪\n",
       "d[qsmk]                                                                        ↪\n",
       "\n",
       "↪                                                                             \n",
-      "↪ race_1,education_3,active_2,smokeintensity,sex_1,smokeyrs,education_2,wt71])\n",
+      "↪ n_5,active_2,smokeintensity,exercise_2,exercise_1,education_3,smokeyrs,age])\n",
       "↪                                                                             \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{qsmk} and U→wt82_71 then P(wt82_71|qsmk,exercise_1,education_4,exercise_2,active_1,age,education_5,race_1,education_3,active_2,smokeintensity,sex_1,smokeyrs,education_2,wt71,U) = P(wt82_71|qsmk,exercise_1,education_4,exercise_2,active_1,age,education_5,race_1,education_3,active_2,smokeintensity,sex_1,smokeyrs,education_2,wt71)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{qsmk} and U→wt82_71 then P(wt82_71|qsmk,sex_1,active_1,education_4,race_1,education_2,wt71,education_5,active_2,smokeintensity,exercise_2,exercise_1,education_3,smokeyrs,age,U) = P(wt82_71|qsmk,sex_1,active_1,education_4,race_1,education_2,wt71,education_5,active_2,smokeintensity,exercise_2,exercise_1,education_3,smokeyrs,age)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -881,13 +521,13 @@
       "Estimand name: general_adjustment\n",
       "Estimand expression:\n",
       "   d                                                                           ↪\n",
-      "───────(E[wt_82_71|education_4,exercise_2,education_2,active_1,age,education_5 ↪\n",
+      "───────(E[wt_82_71|sex_1,active_1,education_4,race_1,education_2,wt71,educatio ↪\n",
       "d[qsmk]                                                                        ↪\n",
       "\n",
       "↪                                                                             \n",
-      "↪ ,race_1,smokeintensity,education_3,active_2,exercise_1,sex_1,smokeyrs,wt71])\n",
+      "↪ n_5,active_2,smokeintensity,exercise_2,exercise_1,education_3,smokeyrs,age])\n",
       "↪                                                                             \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{qsmk} and U→wt82_71 then P(wt82_71|qsmk,education_4,exercise_2,education_2,active_1,age,education_5,race_1,smokeintensity,education_3,active_2,exercise_1,sex_1,smokeyrs,wt71,U) = P(wt82_71|qsmk,education_4,exercise_2,education_2,active_1,age,education_5,race_1,smokeintensity,education_3,active_2,exercise_1,sex_1,smokeyrs,wt71)\n",
+      "Estimand assumption 1, Unconfoundedness: If U→{qsmk} and U→wt82_71 then P(wt82_71|qsmk,sex_1,active_1,education_4,race_1,education_2,wt71,education_5,active_2,smokeintensity,exercise_2,exercise_1,education_3,smokeyrs,age,U) = P(wt82_71|qsmk,sex_1,active_1,education_4,race_1,education_2,wt71,education_5,active_2,smokeintensity,exercise_2,exercise_1,education_3,smokeyrs,age)\n",
       "\n"
      ]
     }
@@ -939,7 +579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 9,
    "id": "830fe396",
    "metadata": {},
    "outputs": [
@@ -948,7 +588,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "[ATE] Linear Regression: 3.381171033988095\n"
+      "[ATE] Linear Regression: 3.3811710339880983\n"
      ]
     }
    ],
@@ -970,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 10,
    "id": "a7aa0ee5",
    "metadata": {},
    "outputs": [
@@ -978,7 +618,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ATE] DR Learner: 3.738829410323152\n"
+      "[ATE] DR Learner: 3.893642417299098\n",
+      "[ATE] DR Learner 95% CI: [[[3.35046186]]\n",
+      "\n",
+      " [[5.20724188]]]\n"
      ]
     }
    ],
@@ -986,16 +629,22 @@
     "estimate_drl = est_model.estimate_effect(\n",
     "    identified_estimand=estimand,\n",
     "    method_name=\"backdoor.econml.dr.DRLearner\",\n",
+    "    control_value=0,\n",
+    "    treatment_value=1,\n",
+    "    target_units=\"ate\",\n",
+    "    confidence_intervals=True,\n",
     "    method_params={\n",
     "        \"init_params\": {\n",
     "            \"model_propensity\": LogisticRegression(max_iter=5000),\n",
     "            \"model_regression\": GradientBoostingRegressor(random_state=42)\n",
     "        },\n",
-    "        \"fit_params\": {}\n",
+    "        \"fit_params\": {\n",
+    "            \"inference\": BootstrapInference(n_bootstrap_samples=500, n_jobs=-1)\n",
+    "        }\n",
     "    }\n",
     ")\n",
     "print(\"[ATE] DR Learner:\", estimate_drl.value)\n",
-    "\n"
+    "print(\"[ATE] DR Learner 95% CI:\", estimate_drl.get_confidence_intervals())\n"
    ]
   },
   {
@@ -1008,7 +657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 11,
    "id": "a91cdd48",
    "metadata": {},
    "outputs": [
@@ -1016,10 +665,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ATE] DML: 3.9002505426347462\n",
-      "[ATE] DML 95% CI: [[[3.19674121]]\n",
+      "[ATE] DML: 3.464128951245904\n",
+      "[ATE] DML 95% CI: [[[2.4019656]]\n",
       "\n",
-      " [[5.20465502]]]\n"
+      " [[4.4395689]]]\n"
      ]
     }
    ],
@@ -1046,6 +695,216 @@
     "print(\"[ATE] DML 95% CI:\", estimate_dml.get_confidence_intervals())\n",
     "\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "869355b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ATE] DML: 3.464128951245904\n"
+     ]
+    }
+   ],
+   "source": [
+    "estimate_dml_fast = est_model.estimate_effect(\n",
+    "    identified_estimand=estimand,\n",
+    "    method_name='backdoor.econml.dml.DML',\n",
+    "    method_params={\n",
+    "        'init_params': {\n",
+    "            'model_y': GradientBoostingRegressor(random_state=42),\n",
+    "            'model_t': GradientBoostingRegressor(random_state=42),\n",
+    "            'model_final': LinearRegression(fit_intercept=False),\n",
+    "        },\n",
+    "        'fit_params': {}}\n",
+    ")\n",
+    "print(\"[ATE] DML:\", estimate_dml.value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60080461",
+   "metadata": {},
+   "source": [
+    "## Refute"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdcda503",
+   "metadata": {},
+   "source": [
+    "### 1. Add Random Common Cause\n",
+    "\n",
+    "데이터셋에 독립적인 무작위 변수를 넣었을 때, 추정값이 바뀌는가?\n",
+    "\n",
+    "- **기대**: New effect ≈ Estimated effect, p > 0.05\n",
+    "\n",
+    "- **해석**: 크게 변하면 → 모델이 잡음에 민감하거나 과적합일 가능성"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "9ad86368",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Refute: Add a random common cause\n",
+      "Estimated effect:3.6087034824487034\n",
+      "New effect:3.4568539723790566\n",
+      "p value:0.42\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "res_random=est_model.refute_estimate(\n",
+    "    estimand,\n",
+    "    estimate_dml_fast,\n",
+    "    method_name=\"random_common_cause\"\n",
+    ")\n",
+    "print(res_random)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd770bcd",
+   "metadata": {},
+   "source": [
+    "### 2. Add Unobserved Common Cause\n",
+    "데이터에 관찰되지 않은 교란요인이 존재한다고 가정했을 때, 추정값이 얼마나 변하는가?\n",
+    "\n",
+    "- **기대**: New effect ≈ Estimated effect\n",
+    "- **해석**:\n",
+    "   - 크게 변하지 않으면 → 잠재적 누락변수(confounder) 에도 견고(robust)\n",
+    "   - 크게 변하면 → 모델이 숨은 교란에 민감, 추가 변수 고려 필요"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "dcce63b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Refute: Add an Unobserved Common Cause\n",
+      "Estimated effect:3.6087034824487034\n",
+      "New effect:3.4630068223974586\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "res_unobserved=est_model.refute_estimate(\n",
+    "    estimand,\n",
+    "    estimate_dml_fast,\n",
+    "    method_name=\"add_unobserved_common_cause\",\n",
+    "    confounders_effect_on_treatment=\"linear\",\n",
+    "    confounders_effect_on_outcome=\"linear\",\n",
+    "    effect_strength_on_treatment=0.01,\n",
+    "    effect_strength_on_outcome=0.02\n",
+    ")\n",
+    "print(res_unobserved)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f01b491",
+   "metadata": {},
+   "source": [
+    "### 3. Placebo Treatment\n",
+    "처치 변수를 무작위로 섞인(permute) 가짜 처치(placebo)로 바꿨을 때, 추정값이 여전히 유의하게 나타나는가?\n",
+    "\n",
+    "- **기대**: New effect ≈ 0, p > 0.05\n",
+    "- **해석**:\n",
+    "   - 효과가 사라지면 → 진짜 처치 효과만 반영된 것 → 견고(robust)\n",
+    "   - 여전히 유의하면 → 모델이 가짜 상관관계(spurious correlation) 를 잡고 있을 가능성"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "545e6293",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Refute: Use a Placebo Treatment\n",
+      "Estimated effect:3.6087034824487034\n",
+      "New effect:-0.01017831576756214\n",
+      "p value:0.4903498144600218\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "res_placebo=est_model.refute_estimate(\n",
+    "    estimand,\n",
+    "    estimate_dml_fast,\n",
+    "    method_name=\"placebo_treatment_refuter\",\n",
+    "    placebo_type=\"permute\",\n",
+    "    num_simulations=10\n",
+    ")\n",
+    "print(res_placebo)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "555163a3",
+   "metadata": {},
+   "source": [
+    "### 4. Use Subset of Data\n",
+    "\n",
+    "데이터의 일부(예: 80%)만 무작위로 남겨두고 추정했을 때, 추정값이 얼마나 안정적인가?\n",
+    "\n",
+    "- **기대**: New effect ≈ Estimated effect, p > 0.05\n",
+    "\n",
+    "- **해석**:\n",
+    "  - 크게 변하지 않으면 → 표본 구성에 강건(robust)\n",
+    "  - 크게 변하면 → 특정 데이터 표본에 민감하거나, 과적합 가능성"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "69481385",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Refute: Use a subset of data\n",
+      "Estimated effect:3.6087034824487034\n",
+      "New effect:3.5177935748179356\n",
+      "p value:0.36231391604195595\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "res_subset=est_model.refute_estimate(\n",
+    "    estimand,\n",
+    "    estimate_dml_fast,\n",
+    "    method_name=\"data_subset_refuter\",\n",
+    "    subset_fraction=0.8,\n",
+    "    num_simulations=10\n",
+    ")\n",
+    "print(res_subset)"
+   ]
   }
  ],
  "metadata": {
diff --git a/book/scm/overview.md b/book/scm/overview.md
index 1a96adb..b45ed91 100644
--- a/book/scm/overview.md
+++ b/book/scm/overview.md
@@ -1,5 +1,5 @@
 # SCM
-구조적 인과 모형(SCM)은 design-based approach와 달리, 인과관계를 DAG로 모델링하여 식별 가정을 명시적으로 표현하는 접근법입니다.
+관찰 데이터를 활용해 ground truth 수준의 인과 메커니즘을 추론하기 위해 SCM(Structural Causal Model)을 사용할 수 있습니다.
 
 SCM을 통한 인과추론 과정은 다음과 같이 요약됩니다:
 

From 40b30a227471c1676d1c56db86f53001340d1a83 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=EC=A1=B0=ED=95=B4=EC=B0=BD?=
 <hc.cho@johaechang-ui-MacBookPro-2.local>
Date: Sat, 18 Oct 2025 18:37:20 +0900
Subject: [PATCH 6/6] =?UTF-8?q?fix:=20=EB=A6=AC=EB=B7=B0=20=EB=B0=98?=
 =?UTF-8?q?=EC=98=81(BDC=20code=20=EC=88=98=EC=A0=95,=20overview=20?=
 =?UTF-8?q?=EC=88=98=EC=A0=95)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .DS_Store                         | Bin 6148 -> 0 bytes
 .gitignore                        |   2 +
 book/scm/backdoor_criterion.ipynb | 162 +++++++++++-------------------
 book/scm/causal_model.png         | Bin 0 -> 83821 bytes
 book/scm/overview.md              |  60 +++++++++--
 5 files changed, 113 insertions(+), 111 deletions(-)
 delete mode 100644 .DS_Store
 create mode 100644 book/scm/causal_model.png

diff --git a/.DS_Store b/.DS_Store
deleted file mode 100644
index dbcfa895b883d20f9e64f4fb66c24a885bf11e8e..0000000000000000000000000000000000000000
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diff --git a/.gitignore b/.gitignore
index ac0cf43..b86db1f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -136,3 +136,5 @@ _build/
 
 # poetry files
 pyproject.toml
+
+.DS_Store
\ No newline at end of file
diff --git a/book/scm/backdoor_criterion.ipynb b/book/scm/backdoor_criterion.ipynb
index e8a2731..b55a9bc 100644
--- a/book/scm/backdoor_criterion.ipynb
+++ b/book/scm/backdoor_criterion.ipynb
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "dd8821be",
    "metadata": {},
    "outputs": [],
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "3f63bbe8",
    "metadata": {},
    "outputs": [
@@ -282,7 +282,7 @@
        "[5 rows x 67 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -320,7 +320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "ff2f4b4b",
    "metadata": {},
    "outputs": [
@@ -358,23 +358,6 @@
     "print(\"처치군/대조군:\\n\", df_clean[treatment].value_counts())"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d985c724",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 범주형 변수 원핫 인코딩\n",
-    "categorical_vars = [\"sex\", \"race\", \"education\", \"active\", \"exercise\"]\n",
-    "df_encoded = pd.get_dummies(df_clean, columns=categorical_vars, drop_first=True)\n",
-    "\n",
-    "# 연속형 변수 스케일링\n",
-    "numeric_confounders = [\"age\", \"smokeintensity\", \"smokeyrs\", \"wt71\"]\n",
-    "scaler = StandardScaler()\n",
-    "df_encoded[numeric_confounders] = scaler.fit_transform(df_encoded[numeric_confounders])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "25ebfea3",
@@ -385,7 +368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 5,
    "id": "65df40db",
    "metadata": {},
    "outputs": [
@@ -401,8 +384,7 @@
     }
    ],
    "source": [
-    "# 시각화용 DAG (원핫 인코딩 전 변수 기준)\n",
-    "gml_graph_viz = \"\"\"\n",
+    "gml_graph = \"\"\"\n",
     "graph [\n",
     "  directed 1\n",
     "\n",
@@ -442,41 +424,12 @@
     "]\n",
     "\"\"\"\n",
     "\n",
-    "cm_for_viz = CausalModel(data=df_clean, treatment=treatment, outcome=outcome, graph=gml_graph_viz)\n",
-    "cm_for_viz.view_model(\n",
+    "cm = CausalModel(data=df_clean, treatment=treatment, outcome=outcome, graph=gml_graph)\n",
+    "cm.view_model(\n",
     "    layout=\"dot\"\n",
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2539d612",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 추정용 DAG\n",
-    "#   - 모든 공변량(원핫 포함) -> {qsmk, wt82_71}\n",
-    "#   - qsmk -> wt82_71\n",
-    "\n",
-    "all_cols = df_encoded.columns.tolist()\n",
-    "confounder_cols = [c for c in all_cols if c not in [treatment, outcome]]\n",
-    "\n",
-    "gml_nodes = []\n",
-    "gml_nodes.append(f'  node [ id \"{treatment}\" label \"{treatment}\" ]')\n",
-    "gml_nodes.append(f'  node [ id \"{outcome}\" label \"{outcome}\" ]')\n",
-    "for c in confounder_cols:\n",
-    "    gml_nodes.append(f'  node [ id \"{c}\" label \"{c}\" ]')\n",
-    "\n",
-    "gml_edges = []\n",
-    "for c in confounder_cols:\n",
-    "    gml_edges.append(f'  edge [ source \"{c}\" target \"{treatment}\" ]')\n",
-    "    gml_edges.append(f'  edge [ source \"{c}\" target \"{outcome}\" ]')\n",
-    "gml_edges.append(f'  edge [ source \"{treatment}\" target \"{outcome}\" ]')\n",
-    "\n",
-    "gml_graph = 'graph [\\n  directed 1\\n\\n' + \"\\n\".join(gml_nodes) + \"\\n\\n\" + \"\\n\".join(gml_edges) + \"\\n]\""
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "5aa85b75",
@@ -487,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "5d714d62",
    "metadata": {},
    "outputs": [
@@ -501,13 +454,13 @@
       "Estimand name: backdoor\n",
       "Estimand expression:\n",
       "   d                                                                           ↪\n",
-      "───────(E[wt_82_71|sex_1,active_1,education_4,race_1,education_2,wt71,educatio ↪\n",
+      "───────(E[wt_82_71|sex,age,smokeyrs,race,active,wt71,education,exercise,smokei ↪\n",
       "d[qsmk]                                                                        ↪\n",
       "\n",
-      "↪                                                                             \n",
-      "↪ n_5,active_2,smokeintensity,exercise_2,exercise_1,education_3,smokeyrs,age])\n",
-      "↪                                                                             \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{qsmk} and U→wt82_71 then P(wt82_71|qsmk,sex_1,active_1,education_4,race_1,education_2,wt71,education_5,active_2,smokeintensity,exercise_2,exercise_1,education_3,smokeyrs,age,U) = P(wt82_71|qsmk,sex_1,active_1,education_4,race_1,education_2,wt71,education_5,active_2,smokeintensity,exercise_2,exercise_1,education_3,smokeyrs,age)\n",
+      "↪           \n",
+      "↪ ntensity])\n",
+      "↪           \n",
+      "Estimand assumption 1, Unconfoundedness: If U→{qsmk} and U→wt82_71 then P(wt82_71|qsmk,sex,age,smokeyrs,race,active,wt71,education,exercise,smokeintensity,U) = P(wt82_71|qsmk,sex,age,smokeyrs,race,active,wt71,education,exercise,smokeintensity)\n",
       "\n",
       "### Estimand : 2\n",
       "Estimand name: iv\n",
@@ -521,20 +474,20 @@
       "Estimand name: general_adjustment\n",
       "Estimand expression:\n",
       "   d                                                                           ↪\n",
-      "───────(E[wt_82_71|sex_1,active_1,education_4,race_1,education_2,wt71,educatio ↪\n",
+      "───────(E[wt_82_71|sex,age,smokeyrs,race,active,wt71,education,exercise,smokei ↪\n",
       "d[qsmk]                                                                        ↪\n",
       "\n",
-      "↪                                                                             \n",
-      "↪ n_5,active_2,smokeintensity,exercise_2,exercise_1,education_3,smokeyrs,age])\n",
-      "↪                                                                             \n",
-      "Estimand assumption 1, Unconfoundedness: If U→{qsmk} and U→wt82_71 then P(wt82_71|qsmk,sex_1,active_1,education_4,race_1,education_2,wt71,education_5,active_2,smokeintensity,exercise_2,exercise_1,education_3,smokeyrs,age,U) = P(wt82_71|qsmk,sex_1,active_1,education_4,race_1,education_2,wt71,education_5,active_2,smokeintensity,exercise_2,exercise_1,education_3,smokeyrs,age)\n",
+      "↪           \n",
+      "↪ ntensity])\n",
+      "↪           \n",
+      "Estimand assumption 1, Unconfoundedness: If U→{qsmk} and U→wt82_71 then P(wt82_71|qsmk,sex,age,smokeyrs,race,active,wt71,education,exercise,smokeintensity,U) = P(wt82_71|qsmk,sex,age,smokeyrs,race,active,wt71,education,exercise,smokeintensity)\n",
       "\n"
      ]
     }
    ],
    "source": [
     "est_model = CausalModel(\n",
-    "    data=df_encoded,\n",
+    "    data=df_clean,\n",
     "    treatment=treatment,\n",
     "    outcome=outcome,\n",
     "    graph=gml_graph\n",
@@ -579,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "830fe396",
    "metadata": {},
    "outputs": [
@@ -588,7 +541,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "[ATE] Linear Regression: 3.3811710339880983\n"
+      "[ATE] Linear Regression: 3.3811710339880823\n"
      ]
     }
    ],
@@ -610,7 +563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "id": "a7aa0ee5",
    "metadata": {},
    "outputs": [
@@ -618,10 +571,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ATE] DR Learner: 3.893642417299098\n",
-      "[ATE] DR Learner 95% CI: [[[3.35046186]]\n",
+      "[ATE] DR Learner: 3.8479455513705227\n",
+      "[ATE] DR Learner 95% CI: [[[3.12167472]]\n",
       "\n",
-      " [[5.20724188]]]\n"
+      " [[5.29732315]]]\n"
      ]
     }
    ],
@@ -657,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "id": "a91cdd48",
    "metadata": {},
    "outputs": [
@@ -665,10 +618,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ATE] DML: 3.464128951245904\n",
-      "[ATE] DML 95% CI: [[[2.4019656]]\n",
+      "[ATE] DML: 3.8611289087159135\n",
+      "[ATE] DML 95% CI: [[[3.06774555]]\n",
       "\n",
-      " [[4.4395689]]]\n"
+      " [[5.17092571]]]\n"
      ]
     }
    ],
@@ -696,17 +649,25 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "60080461",
+   "metadata": {},
+   "source": [
+    "## Refute"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "869355b3",
+   "execution_count": 15,
+   "id": "18481fc5",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ATE] DML: 3.464128951245904\n"
+      "[ATE] DML: 3.8611289087159135\n"
      ]
     }
    ],
@@ -725,14 +686,6 @@
     "print(\"[ATE] DML:\", estimate_dml.value)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "60080461",
-   "metadata": {},
-   "source": [
-    "## Refute"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "bdcda503",
@@ -749,7 +702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 11,
    "id": "9ad86368",
    "metadata": {},
    "outputs": [
@@ -758,9 +711,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Add a random common cause\n",
-      "Estimated effect:3.6087034824487034\n",
-      "New effect:3.4568539723790566\n",
-      "p value:0.42\n",
+      "Estimated effect:3.5111934415796173\n",
+      "New effect:3.4686090208687586\n",
+      "p value:0.72\n",
       "\n"
      ]
     }
@@ -785,12 +738,13 @@
     "- **기대**: New effect ≈ Estimated effect\n",
     "- **해석**:\n",
     "   - 크게 변하지 않으면 → 잠재적 누락변수(confounder) 에도 견고(robust)\n",
-    "   - 크게 변하면 → 모델이 숨은 교란에 민감, 추가 변수 고려 필요"
+    "   - 크게 변하면 → 모델이 숨은 교란에 민감, 추가 변수 고려 필요\n",
+    "- **참고**: 도메인 지식을 기반으로, 교란이 처리변수와 결과에 미치는 영향의 크기는 사용자가 직접 설정해야 합니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 12,
    "id": "dcce63b4",
    "metadata": {},
    "outputs": [
@@ -799,8 +753,8 @@
      "output_type": "stream",
      "text": [
       "Refute: Add an Unobserved Common Cause\n",
-      "Estimated effect:3.6087034824487034\n",
-      "New effect:3.4630068223974586\n",
+      "Estimated effect:3.5111934415796173\n",
+      "New effect:2.993841050044186\n",
       "\n"
      ]
     }
@@ -834,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 13,
    "id": "545e6293",
    "metadata": {},
    "outputs": [
@@ -843,9 +797,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a Placebo Treatment\n",
-      "Estimated effect:3.6087034824487034\n",
-      "New effect:-0.01017831576756214\n",
-      "p value:0.4903498144600218\n",
+      "Estimated effect:3.5111934415796173\n",
+      "New effect:0.14283539321647556\n",
+      "p value:0.3866345448655441\n",
       "\n"
      ]
     }
@@ -879,7 +833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 14,
    "id": "69481385",
    "metadata": {},
    "outputs": [
@@ -888,9 +842,9 @@
      "output_type": "stream",
      "text": [
       "Refute: Use a subset of data\n",
-      "Estimated effect:3.6087034824487034\n",
-      "New effect:3.5177935748179356\n",
-      "p value:0.36231391604195595\n",
+      "Estimated effect:3.5111934415796173\n",
+      "New effect:3.4052136015402126\n",
+      "p value:0.4004970164482444\n",
       "\n"
      ]
     }
diff --git a/book/scm/causal_model.png b/book/scm/causal_model.png
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diff --git a/book/scm/overview.md b/book/scm/overview.md
index b45ed91..c989e17 100644
--- a/book/scm/overview.md
+++ b/book/scm/overview.md
@@ -1,9 +1,55 @@
-# SCM
-관찰 데이터를 활용해 ground truth 수준의 인과 메커니즘을 추론하기 위해 SCM(Structural Causal Model)을 사용할 수 있습니다.
+# Structural Causal Model(SCM)
 
-SCM을 통한 인과추론 과정은 다음과 같이 요약됩니다:
+SCM(Structural Causal Model)은 변수 간의 causal mechanism을 구조방정식과 DAG로 표현하는 접근입니다.  
+이 섹션에서는 주요 식별 전략을 중심으로, 실제 데이터를 활용한 인과효과 분석을 다룹니다.
 
-1. **Model**: 변수들 사이의 인과적 관계를 DAG로 정의하고, 혼란변수(confounder), 매개변수(mediator), 충돌변수(collider) 등을 구분합니다.
-2. **Identify**: 관심 있는 인과효과가 그래프에서 식별 가능한지 검토하고, Back-door, Front-door, 도구변수(IV)와 같은 식별 전략을 탐색합니다.
-3. **Estimate**: 식별 전략에 적합한 통계적 방법으로 인과효과를 추정합니다.
-4. **Refute**: 위약(placebo) 분석, 무작위 교란(Randomization Test) 등을 통해 추정된 인과효과의 강건성을 검증합니다.
\ No newline at end of file
+---
+
+## 1. Backdoor Criterion
+
+- **식별 전략:**  
+  원인과 결과 모두에 영향을 미치는 교란변수(confounder) 를 통제하여,  
+  인과효과를 다음과 같이 식별합니다.
+
+  $$
+  P(Y \mid do(X)) = \sum_Z P(Y \mid X, Z) P(Z)
+  $$
+
+- **데이터:** *National Health and Nutrition Examination Survey (NHEFS)*
+
+---
+
+## 2. Frontdoor Criterion
+
+- **식별 전략:**  
+  원인($X$)이 결과($Y$)에 미치는 효과가 매개변수($M$)을 통해서만 전달될 때,  
+  다음 식을 통해 인과효과를 간접적으로 식별합니다.
+
+  $$
+  P(Y \mid do(X)) = \sum_M P(M \mid X) \sum_{X'} P(Y \mid M, X') P(X')
+  $$
+
+- **데이터:** *NYC TLC 2023 High Volume FHV Trip Records*
+
+---
+
+## 3. Instrument Variable (IV)
+
+- **식별 전략:**  
+  ($X$)와 ($Y$) 간의 내생성(endogeneity) 문제를 해결하기 위해,
+  ($X$)에는 영향을 주지만 ($Y$) 에는 직접 영향을 미치지 않는 도구변수($Z$)를 사용합니다.  
+  인과효과는 다음과 같이 식별됩니다.
+
+  $$
+  \hat{\beta}_{IV} = \frac{\operatorname{Cov}(Z, Y)}{\operatorname{Cov}(Z, X)}
+  $$
+- **데이터:** 추후 확정 예정
+
+---
+
+## 4. Causal Discovery
+
+- **목표:**  
+  변수 간 조건부 독립성(Conditional Independence)을 이용해 데이터로부터 DAG 구조를 학습하고 인과 방향성을 탐색합니다.
+
+- **데이터:** 추후 확정 예정