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fix: markdown cell
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‎book/scm/causal_discovery.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 289,
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"execution_count": 2,
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"id": "82d3b359",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
@@ -64,10 +73,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": 290,
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"execution_count": 3,
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"id": "978da1a6",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/var/folders/fw/d6t_wpzj2zb9lyd3st5l4dfm0000gn/T/ipykernel_39769/3414311417.py:1: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
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" data_mpg = pd.read_csv(\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"4 70.0 1.0 ford torino "
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]
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"execution_count": 3,
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}
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},
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{
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"4 17.0 8.0 302.0 140.0 3449.0 10.5"
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]
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},
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}
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "f7f27d2d",
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"outputs": [
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"LiNGAM adjacency matrix shape: (6, 6)\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.10/site-packages/sklearn/linear_model/_base.py:280: RuntimeWarning: divide by zero encountered in matmul\n",
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" return X @ coef_ + self.intercept_\n",
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"/Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.10/site-packages/sklearn/linear_model/_base.py:280: RuntimeWarning: overflow encountered in matmul\n",
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" return X @ coef_ + self.intercept_\n",
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"/Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.10/site-packages/sklearn/linear_model/_base.py:280: RuntimeWarning: invalid value encountered in matmul\n",
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" return X @ coef_ + self.intercept_\n",
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"/Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.10/site-packages/sklearn/linear_model/_base.py:280: RuntimeWarning: divide by zero encountered in matmul\n",
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" return X @ coef_ + self.intercept_\n",
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"/Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.10/site-packages/sklearn/linear_model/_base.py:280: RuntimeWarning: overflow encountered in matmul\n",
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" return X @ coef_ + self.intercept_\n",
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"/Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.10/site-packages/sklearn/linear_model/_base.py:280: RuntimeWarning: invalid value encountered in matmul\n",
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" return X @ coef_ + self.intercept_\n",
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"/Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.10/site-packages/sklearn/linear_model/_base.py:280: RuntimeWarning: divide by zero encountered in matmul\n",
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" return X @ coef_ + self.intercept_\n",
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"/Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.10/site-packages/sklearn/linear_model/_base.py:280: RuntimeWarning: overflow encountered in matmul\n",
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" return X @ coef_ + self.intercept_\n",
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"/Users/hc.cho/Projects/awesome-causal-inference-python/.venv/lib/python3.10/site-packages/sklearn/linear_model/_base.py:280: RuntimeWarning: invalid value encountered in matmul\n",
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" return X @ coef_ + self.intercept_\n"
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]
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},
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{
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"data": {
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"image/svg+xml": [
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"</svg>\n"
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],
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"text/plain": [
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"<graphviz.graphs.Digraph at 0x345934dc0>"
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"<graphviz.graphs.Digraph at 0x10f8b08e0>"
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]
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},
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},
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"Depth=3, working on node 5: 100%|██████████| 6/6 [00:00<00:00, 2326.51it/s]"
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"Depth=3, working on node 5: 100%|██████████| 6/6 [00:00<00:00, 2269.44it/s]"
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{
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"</svg>\n"
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],
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"text/plain": [
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"<graphviz.graphs.Digraph at 0x330fbf0a0>"
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"<graphviz.graphs.Digraph at 0x10ae15360>"
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]
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},
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"execution_count": 9,
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"text": [
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"PC edges (±1은 방향/존재 여부 코드):\n"
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"PC edges:\n"
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]
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},
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{
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" \"weight\", key=lambda s: s.abs(), ascending=False\n",
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")\n",
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"\n",
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"print(\"PC edges (±1은 방향/존재 여부 코드):\")\n",
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"print(\"PC edges:\")\n",
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"display(pc_edges_sorted)"
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},
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"outputs": [
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"</svg>\n"
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],
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"text/plain": [
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"<graphviz.graphs.Digraph at 0x33547ba90>"
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"<graphviz.graphs.Digraph at 0x10b179630>"
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},
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"execution_count": 12,
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"text": [
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"GES edges (±1은 방향/존재 여부 코드):\n"
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"GES edges:\n"
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{
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" \"weight\", key=lambda s: s.abs(), ascending=False\n",
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"\n",
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"print(\"GES edges (±1은 방향/존재 여부 코드):\")\n",
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"print(\"GES edges:\")\n",
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"display(ges_edges_sorted)\n"
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},
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"source": [
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"## Identify and Estimate\n",
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"\n",
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"**weight → mpg** 인과 효과를 추정하는 두 가지 경로를 비교합니다.\n",
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"weight → mpg 인과 효과를 추정하는 두 가지 경로를 비교합니다.\n",
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"\n",
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"1. LiNGAM이 제안한 DAG를 그대로 쓴 경우 \n",
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"2. 도메인 지식으로 구성한 DAG를 쓴 경우"
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},
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},
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{
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"=== Identified estimand (LiNGAM DAG 기반) ===\n",
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"=== Identified estimand LiNGAM DAG ===\n",
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"Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
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"\n",
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"### Estimand : 1\n",
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"print(\"=== Identified estimand (LiNGAM DAG 기반) ===\")\n",
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"print(\"=== Identified estimand LiNGAM DAG ===\")\n",
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"print(identified_estimand_lingam)"
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"estimate_lingam = cm_lingam.estimate_effect(\n",
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" method_name=\"backdoor.linear_regression\",\n",
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" # \"weight\"가 1 단위 증가할 때 mpg가 얼마나 변하는지\n",
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" control_value=0,\n",
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" - `horsepower → mpg` (고출력 엔진은 연비가 낮을 가능성)\n",
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"\n",
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"- 출력 → 가속 성능\n",
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" - `horsepower → acceleration` (마력이 높을수록 0–60 시간이 짧아짐)"
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" - `horsepower → acceleration` (마력이 높을수록 가속 시간이 짧아짐)"
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{
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"data": {
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"text/plain": [
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"<dowhy.causal_model.CausalModel at 0x330e3ef50>"
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"<dowhy.causal_model.CausalModel at 0x169ab6110>"
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]
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"=== Identified Estimand (Domain DAG) ===\n",
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"=== Identified Estimand Domain DAG ===\n",
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"Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
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"\n",
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"### Estimand : 1\n",
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" proceed_when_unidentifiable=True\n",
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")\n",
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"print(\"=== Identified Estimand (Domain DAG) ===\")\n",
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"print(\"=== Identified Estimand Domain DAG ===\")\n",
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"print(identified_estimand_domain)\n"
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]
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"estimate_domain = cm_domain.estimate_effect(\n",
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" identified_estimand_domain,\n",
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" method_name=\"backdoor.linear_regression\",\n",
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" # 동일하게 weight가 1 단위 증가할 때의 효과\n",
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{
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"execution_count": null,
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"id": "d4c9560c",
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"id": "b8ec4f40",
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"metadata": {},
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"outputs": [],
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"source": []

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