refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-causal-inference
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title: Causal Inference
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [인과 추론, causal inference, do-calculus, Pearl, DAG, counterfactual, SCM, RCT, propensity score]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.93
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verification_status: applied
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tags: [statistics, causal-inference, dag, do-calculus, pearl, ab-testing, dowhy, econml, observational-study]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python / R
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framework: DoWhy / EconML / CausalML / pgmpy
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---
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# Causal Inference
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## 📌 한 줄 통찰
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> **"매 correlation 의 X — 매 cause"**. 매 Judea Pearl 의 ladder. 매 observational 의 limit + 매 RCT / DAG / counterfactual 의 fix. 매 modern AI 의 base capability — 매 LLM 의 weakest area. 매 policy / medical / business 의 critical.
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## 📖 핵심
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### Pearl's Ladder of Causation
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1. **Association** (P(y|x)): 매 correlation. 매 standard ML.
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2. **Intervention** (P(y|do(x))): 매 "what if I change x?".
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3. **Counterfactual** (P(y_x|x', y')): 매 "what would have happened if?".
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→ 매 LLM 의 mostly stuck on step 1.
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### 매 핵심 concept
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#### Confounder
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- 매 X → Y 매 spurious 의 매 Z (common cause).
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- 예: 매 ice cream sales ↔ drowning (Z = 매 summer).
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#### Mediator
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- 매 X → M → Y.
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#### Collider
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- 매 X → Z ← Y.
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- 매 Z 의 condition 의 spurious correlation 의 induce!
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#### Backdoor path
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- 매 X ← Z → Y.
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- 매 Z 의 control 의 close.
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#### Frontdoor
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- 매 X → M → Y (매 confounder 가 매 X-Y 간 의 unobserved).
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### 매 method
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#### RCT (gold standard)
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- 매 randomization 의 confounder 의 break.
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- 매 ethics / cost.
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#### Observational + adjustment
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- **Propensity Score Matching (PSM)**.
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- **Inverse Probability Weighting (IPW)**.
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- **Regression discontinuity (RDD)**.
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- **Difference-in-differences (DiD)**.
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- **Instrumental variables (IV)**.
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- **Synthetic control**.
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#### Causal graph (DAG)
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- 매 explicit assumption.
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- 매 do-calculus 의 identify.
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#### ML-based
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- **Causal forest** (Wager-Athey).
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- **Double ML** (Chernozhukov).
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- **CausalGAN / counterfactual VAE**.
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### 매 Simpson's paradox
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- 매 aggregate vs subgroup 의 reverse.
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- 매 Berkeley admission, 매 kidney stone treatment.
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- → 매 confounder 의 stratify.
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### 매 응용
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1. **A/B test** + 매 follow-up causal.
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2. **Pricing**: 매 price → 매 demand.
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3. **Marketing attribution**: 매 channel → 매 conversion.
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4. **Medicine**: 매 treatment effect.
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5. **Policy**: 매 minimum wage.
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6. **Education**: 매 program effect.
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7. **Recommender**: 매 click ≠ 매 caused conversion.
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### 매 modern tool
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- **DoWhy** (Microsoft): 매 4-step framework.
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- **EconML** (Microsoft).
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- **CausalML** (Uber).
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- **pgmpy**: 매 graphical model.
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- **GeNIe / Hugin**: 매 visual.
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- **DAGitty**: 매 web DAG.
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### 매 LLM 의 한계
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- 매 association 의 strong.
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- 매 spurious 의 confidently 의 emit.
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- 매 causal reasoning 의 weak.
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- 매 hybrid (LLM + symbolic causal) 의 trend.
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## 💻 패턴
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### DoWhy (4-step framework)
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```python
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from dowhy import CausalModel
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model = CausalModel(
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data=df,
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treatment='ad_exposure',
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outcome='conversion',
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common_causes=['age', 'income', 'past_purchases'],
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)
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# 1. Identify
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estimand = model.identify_effect(proceed_when_unidentifiable=False)
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print(estimand)
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# 2. Estimate
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estimate = model.estimate_effect(estimand, method_name='backdoor.propensity_score_matching')
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print(estimate.value)
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# 3. Refute
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refutation = model.refute_estimate(estimand, estimate, method_name='placebo_treatment_refuter')
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print(refutation)
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```
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### Propensity Score Matching
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```python
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from sklearn.linear_model import LogisticRegression
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from sklearn.neighbors import NearestNeighbors
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# 매 propensity score = P(treatment=1 | covariates)
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ps_model = LogisticRegression()
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ps_model.fit(X_covariates, treatment)
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ps = ps_model.predict_proba(X_covariates)[:, 1]
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# 매 match treated to control
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treated = df[df.treatment == 1]
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control = df[df.treatment == 0]
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knn = NearestNeighbors(n_neighbors=1).fit(ps[control.index].reshape(-1, 1))
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matches = knn.kneighbors(ps[treated.index].reshape(-1, 1), return_distance=False)
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# 매 ATE estimate
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ate = treated.outcome.mean() - control.iloc[matches.flatten()].outcome.mean()
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```
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### IPW (Inverse Probability Weighting)
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```python
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def ipw_ate(df, treatment, outcome, ps):
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weight = np.where(df[treatment] == 1, 1 / ps, 1 / (1 - ps))
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treated_avg = (df[outcome] * df[treatment] * weight).sum() / weight[df[treatment] == 1].sum()
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control_avg = (df[outcome] * (1 - df[treatment]) * weight).sum() / weight[df[treatment] == 0].sum()
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return treated_avg - control_avg
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```
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### Difference-in-Differences (DiD)
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```python
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import statsmodels.api as sm
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# 매 panel data: pre/post × treatment/control
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df['post'] = (df['period'] >= treatment_period).astype(int)
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df['treated'] = (df['group'] == 'treated').astype(int)
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df['interaction'] = df['post'] * df['treated']
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model = sm.OLS(df['outcome'], sm.add_constant(df[['post', 'treated', 'interaction']])).fit()
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# 매 interaction coefficient = 매 DiD treatment effect
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print(model.summary())
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```
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### Causal Forest (heterogeneous treatment effect)
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```python
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from econml.dml import CausalForestDML
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cf = CausalForestDML(
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n_estimators=200,
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discrete_treatment=True,
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random_state=42,
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)
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cf.fit(Y=df['outcome'], T=df['treatment'], X=df[features], W=df[confounders])
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# 매 individual treatment effect
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ites = cf.effect(df_test[features])
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# 매 confidence interval
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lower, upper = cf.effect_interval(df_test[features], alpha=0.05)
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```
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### DAG + Pearl's do-calculus (pgmpy)
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```python
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from pgmpy.models import BayesianNetwork
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from pgmpy.factors.discrete import TabularCPD
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from pgmpy.inference.CausalInference import CausalInference
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# 매 X → Y, X → Z → Y
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model = BayesianNetwork([('X', 'Y'), ('X', 'Z'), ('Z', 'Y')])
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# ... add CPDs ...
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ci = CausalInference(model)
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# 매 P(Y | do(X = 1))
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result = ci.query(variables=['Y'], do={'X': 1})
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print(result)
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```
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### Synthetic control (state policy effect)
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```python
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# 매 weighted combination of control units 의 treated 의 mimic
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from synthetic_control import SyntheticControl # 매 hypothetical lib
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sc = SyntheticControl(
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treated_unit='California',
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control_pool=other_states,
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pre_period=range(1990, 2000),
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post_period=range(2000, 2010),
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)
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sc.fit(predictors=['gdp', 'unemployment', 'income'])
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effect = sc.treatment_effect()
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```
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### Refutation (sensitivity analysis)
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```python
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from dowhy import CausalModel
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# 1. Placebo treatment
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refute_placebo = model.refute_estimate(
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estimand, estimate, method_name='placebo_treatment_refuter',
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)
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# 매 effect 의 0 가까이 → 매 robust.
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# 2. Random common cause
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refute_random = model.refute_estimate(
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estimand, estimate, method_name='random_common_cause',
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)
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# 3. Data subset
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refute_subset = model.refute_estimate(
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estimand, estimate, method_name='data_subset_refuter',
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)
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```
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### Simpson's paradox detector
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```python
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def detect_simpson(df, x_col, y_col, group_col):
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# 매 aggregate
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overall_corr = df[[x_col, y_col]].corr().iloc[0, 1]
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# 매 subgroup
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subgroup_corrs = df.groupby(group_col).apply(
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lambda g: g[[x_col, y_col]].corr().iloc[0, 1]
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)
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if overall_corr > 0 and (subgroup_corrs < 0).all():
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return f"Simpson's paradox: overall +, subgroups all -"
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if overall_corr < 0 and (subgroup_corrs > 0).all():
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return f"Simpson's paradox: overall -, subgroups all +"
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return None
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```
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## 🤔 결정 기준
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| 상황 | Method |
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|---|---|
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| New feature launch | A/B test (RCT) |
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| Historical data | DoWhy + matching |
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| Heterogeneous effect | Causal Forest |
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| Panel data | DiD |
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| Cutoff threshold | RDD |
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| Hidden confounder + IV | Instrumental Variables |
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| Single treated unit | Synthetic Control |
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| ML-aware confounder | Double ML |
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**기본값**: 매 RCT first. 매 observational 가 DoWhy + sensitivity refute.
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## 🔗 Graph
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- 부모: [[Statistics]] · [[Decision Theory]]
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- 변형: [[DAG]] · [[Do-Calculus]] · [[Counterfactual]]
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- Adjacent: [[Bayesian Statistics]] · [[Anthropic-Principle]] · [[Beliefs]] · [[Algorithmic Fairness]]
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## 🤖 LLM 활용
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**언제**: 매 policy decision. 매 marketing attribution. 매 medical treatment. 매 root cause analysis. 매 fairness counterfactual.
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**언제 X**: 매 pure prediction (ML 의 OK). 매 LLM 의 alone (weak on step 2-3).
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## ❌ 안티패턴
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- **Correlation = causation**: 매 classic mistake.
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- **Collider 의 control**: 매 spurious correlation 의 induce.
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- **No DAG**: 매 hidden assumption.
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- **Single method**: 매 sensitivity 의 X.
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- **No refutation**: 매 fragile estimate.
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- **Simpson's paradox 의 unaware**: 매 misleading.
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- **LLM 의 causal claim 의 trust**: 매 association level 만.
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## 🧪 검증 / 중복
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- Verified (Pearl "Book of Why", Hernán "Causal Inference: What If", DoWhy paper).
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- 신뢰도 A.
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- Related: [[Bayesian Statistics]] · [[Algorithmic Fairness]] · [[Bias-Correction-Algorithm]] · [[A/B Testing]] · [[Anthropic-Principle]].
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — Pearl ladder + DAG + 매 DoWhy / PSM / DiD / Causal Forest code |
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