2nd/10_Wiki/Topics/AI_and_ML/Causal-Inference.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-causal-inference	Causal Inference	10_Wiki/Topics	verified	self	인과 추론 causal inference do-calculus Pearl DAG counterfactual SCM RCT propensity score	none	A	0.93	applied	statistics causal-inference dag do-calculus pearl ab-testing dowhy econml observational-study		2026-05-10	pending	language framework Python / R DoWhy / EconML / CausalML / pgmpy

Causal Inference

📌 한 줄 통찰

"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.

📖 핵심

Pearl's Ladder of Causation

1. Association (P(y|x)): 매 correlation. 매 standard ML.
2. Intervention (P(y|do(x))): 매 "what if I change x?".
3. Counterfactual (P(y_x|x', y')): 매 "what would have happened if?".

→ 매 LLM 의 mostly stuck on step 1.

매 핵심 concept

Confounder

• 매 X → Y 매 spurious 의 매 Z (common cause).
• 예: 매 ice cream sales ↔ drowning (Z = 매 summer).

Mediator

• 매 X → M → Y.

Collider

• 매 X → Z ← Y.
• 매 Z 의 condition 의 spurious correlation 의 induce!

Backdoor path

• 매 X ← Z → Y.
• 매 Z 의 control 의 close.

Frontdoor

• 매 X → M → Y (매 confounder 가 매 X-Y 간 의 unobserved).

매 method

RCT (gold standard)

• 매 randomization 의 confounder 의 break.
• 매 ethics / cost.

Observational + adjustment

• Propensity Score Matching (PSM).
• Inverse Probability Weighting (IPW).
• Regression discontinuity (RDD).
• Difference-in-differences (DiD).
• Instrumental variables (IV).
• Synthetic control.

Causal graph (DAG)

• 매 explicit assumption.
• 매 do-calculus 의 identify.

ML-based

• Causal forest (Wager-Athey).
• Double ML (Chernozhukov).
• CausalGAN / counterfactual VAE.

매 Simpson's paradox

• 매 aggregate vs subgroup 의 reverse.
• 매 Berkeley admission, 매 kidney stone treatment.
• → 매 confounder 의 stratify.

매 응용

1. A/B test + 매 follow-up causal.
2. Pricing: 매 price → 매 demand.
3. Marketing attribution: 매 channel → 매 conversion.
4. Medicine: 매 treatment effect.
5. Policy: 매 minimum wage.
6. Education: 매 program effect.
7. Recommender: 매 click ≠ 매 caused conversion.

매 modern tool

• DoWhy (Microsoft): 매 4-step framework.
• EconML (Microsoft).
• CausalML (Uber).
• pgmpy: 매 graphical model.
• GeNIe / Hugin: 매 visual.
• DAGitty: 매 web DAG.

매 LLM 의 한계

• 매 association 의 strong.
• 매 spurious 의 confidently 의 emit.
• 매 causal reasoning 의 weak.
• 매 hybrid (LLM + symbolic causal) 의 trend.

💻 패턴

DoWhy (4-step framework)

from dowhy import CausalModel

model = CausalModel(
    data=df,
    treatment='ad_exposure',
    outcome='conversion',
    common_causes=['age', 'income', 'past_purchases'],
)

# 1. Identify
estimand = model.identify_effect(proceed_when_unidentifiable=False)
print(estimand)

# 2. Estimate
estimate = model.estimate_effect(estimand, method_name='backdoor.propensity_score_matching')
print(estimate.value)

# 3. Refute
refutation = model.refute_estimate(estimand, estimate, method_name='placebo_treatment_refuter')
print(refutation)

Propensity Score Matching

from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import NearestNeighbors

# 매 propensity score = P(treatment=1 | covariates)
ps_model = LogisticRegression()
ps_model.fit(X_covariates, treatment)
ps = ps_model.predict_proba(X_covariates)[:, 1]

# 매 match treated to control
treated = df[df.treatment == 1]
control = df[df.treatment == 0]

knn = NearestNeighbors(n_neighbors=1).fit(ps[control.index].reshape(-1, 1))
matches = knn.kneighbors(ps[treated.index].reshape(-1, 1), return_distance=False)

# 매 ATE estimate
ate = treated.outcome.mean() - control.iloc[matches.flatten()].outcome.mean()

IPW (Inverse Probability Weighting)

def ipw_ate(df, treatment, outcome, ps):
    weight = np.where(df[treatment] == 1, 1 / ps, 1 / (1 - ps))
    treated_avg = (df[outcome] * df[treatment] * weight).sum() / weight[df[treatment] == 1].sum()
    control_avg = (df[outcome] * (1 - df[treatment]) * weight).sum() / weight[df[treatment] == 0].sum()
    return treated_avg - control_avg

Difference-in-Differences (DiD)

import statsmodels.api as sm

# 매 panel data: pre/post × treatment/control
df['post'] = (df['period'] >= treatment_period).astype(int)
df['treated'] = (df['group'] == 'treated').astype(int)
df['interaction'] = df['post'] * df['treated']

model = sm.OLS(df['outcome'], sm.add_constant(df[['post', 'treated', 'interaction']])).fit()
# 매 interaction coefficient = 매 DiD treatment effect
print(model.summary())

Causal Forest (heterogeneous treatment effect)

from econml.dml import CausalForestDML

cf = CausalForestDML(
    n_estimators=200,
    discrete_treatment=True,
    random_state=42,
)
cf.fit(Y=df['outcome'], T=df['treatment'], X=df[features], W=df[confounders])

# 매 individual treatment effect
ites = cf.effect(df_test[features])

# 매 confidence interval
lower, upper = cf.effect_interval(df_test[features], alpha=0.05)

DAG + Pearl's do-calculus (pgmpy)

from pgmpy.models import BayesianNetwork
from pgmpy.factors.discrete import TabularCPD
from pgmpy.inference.CausalInference import CausalInference

# 매 X → Y, X → Z → Y
model = BayesianNetwork([('X', 'Y'), ('X', 'Z'), ('Z', 'Y')])
# ... add CPDs ...

ci = CausalInference(model)

# 매 P(Y | do(X = 1))
result = ci.query(variables=['Y'], do={'X': 1})
print(result)

Synthetic control (state policy effect)

# 매 weighted combination of control units 의 treated 의 mimic
from synthetic_control import SyntheticControl  # 매 hypothetical lib

sc = SyntheticControl(
    treated_unit='California',
    control_pool=other_states,
    pre_period=range(1990, 2000),
    post_period=range(2000, 2010),
)
sc.fit(predictors=['gdp', 'unemployment', 'income'])
effect = sc.treatment_effect()

Refutation (sensitivity analysis)

from dowhy import CausalModel

# 1. Placebo treatment
refute_placebo = model.refute_estimate(
    estimand, estimate, method_name='placebo_treatment_refuter',
)
# 매 effect 의 0 가까이 → 매 robust.

# 2. Random common cause
refute_random = model.refute_estimate(
    estimand, estimate, method_name='random_common_cause',
)

# 3. Data subset
refute_subset = model.refute_estimate(
    estimand, estimate, method_name='data_subset_refuter',
)

Simpson's paradox detector

def detect_simpson(df, x_col, y_col, group_col):
    # 매 aggregate
    overall_corr = df[[x_col, y_col]].corr().iloc[0, 1]
    
    # 매 subgroup
    subgroup_corrs = df.groupby(group_col).apply(
        lambda g: g[[x_col, y_col]].corr().iloc[0, 1]
    )
    
    if overall_corr > 0 and (subgroup_corrs < 0).all():
        return f"Simpson's paradox: overall +, subgroups all -"
    if overall_corr < 0 and (subgroup_corrs > 0).all():
        return f"Simpson's paradox: overall -, subgroups all +"
    return None

🤔 결정 기준

상황	Method
New feature launch	A/B test (RCT)
Historical data	DoWhy + matching
Heterogeneous effect	Causal Forest
Panel data	DiD
Cutoff threshold	RDD
Hidden confounder + IV	Instrumental Variables
Single treated unit	Synthetic Control
ML-aware confounder	Double ML

기본값: 매 RCT first. 매 observational 가 DoWhy + sensitivity refute.

🔗 Graph

• 부모: Statistics · Decision Theory
• 변형: DAG · Do-Calculus · Counterfactual
• Adjacent: Bayesian Statistics · Anthropic-Principle · Beliefs · Algorithmic Fairness

🤖 LLM 활용

언제: 매 policy decision. 매 marketing attribution. 매 medical treatment. 매 root cause analysis. 매 fairness counterfactual. 언제 X: 매 pure prediction (ML 의 OK). 매 LLM 의 alone (weak on step 2-3).

❌ 안티패턴

• Correlation = causation: 매 classic mistake.
• Collider 의 control: 매 spurious correlation 의 induce.
• No DAG: 매 hidden assumption.
• Single method: 매 sensitivity 의 X.
• No refutation: 매 fragile estimate.
• Simpson's paradox 의 unaware: 매 misleading.
• LLM 의 causal claim 의 trust: 매 association level 만.

🧪 검증 / 중복

• Verified (Pearl "Book of Why", Hernán "Causal Inference: What If", DoWhy paper).
• 신뢰도 A.
• Related: Bayesian Statistics · Algorithmic Fairness · Bias-Correction-Algorithm · A/B Testing · Anthropic-Principle.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — Pearl ladder + DAG + 매 DoWhy / PSM / DiD / Causal Forest code