10-jill0099-causal-inference-mixtape

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Summary

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SKILL.md

Causal Inference: The Mixtape — Code Skill

Practitioner-oriented causal inference skill built from Scott Cunningham's Causal Inference: The Mixtape repository. Covers 10 identification strategies with ready-to-run code templates in Python, R, and Stata.

Methods Covered

Method	Python	R	Stata	Reference
OLS / Regression	statsmodels	estimatr	reg/reghdfe	`references/method-patterns.md` §1
Difference-in-Differences	statsmodels + C()	lfe/fixest	xtreg/reghdfe	`references/method-patterns.md` §2
Event Study (Dynamic DiD)	manual lead/lag	estimatr	reghdfe	`references/method-patterns.md` §3
Staggered DiD / TWFE	statsmodels	bacondecomp	bacondecomp	`references/method-patterns.md` §4
Regression Discontinuity	statsmodels polynomial	rdrobust	rdplot/rdrobust	`references/method-patterns.md` §5
Instrumental Variables	linearmodels IV2SLS	AER/ivreg	ivregress 2sls	`references/method-patterns.md` §6
Synthetic Control	rpy2 → R Synth	Synth + SCtools	synth	`references/method-patterns.md` §7
Matching / PSM / IPW	manual logit + weights	MatchIt + Zelig	teffects/cem	`references/method-patterns.md` §8

Scenario	Recommendation
ML pipeline integration	Python (statsmodels + linearmodels)
Synthetic Control	R (Synth package) or Stata (synth) — Python lacks mature implementation
Bacon decomposition	R (bacondecomp) or Stata — no Python equivalent
Publication-ready tables	Stata (outreg2/esttab) or R (stargazer/modelsummary)
Coarsened Exact Matching	Stata (cem) or R (MatchIt) — no Python equivalent
Quick prototyping	Python with statsmodels

Task	Python	R	Stata
OLS with robust SE	`smf.ols().fit(cov_type='HC1')`	`lm_robust()`	`reg y x, robust`
Cluster SE	`fit(cov_type='cluster', cov_kwds={'groups': g})`	`felm(y ~ x	0
Two-way FE	`C(id) + C(time)` in formula	`felm(y ~ x	id + time)`
IV / 2SLS	`IV2SLS.from_formula('y ~ 1 + exog + [endog ~ inst]')`	`ivreg(y ~ exog	inst)`
DiD	`C(treat)*C(post)`	`treat:post` in formula	`did_multiplegt` or interaction

import statsmodels.formula.api as smf

model = smf.ols('y ~ C(treated)*C(post) + controls', data=df)
results = model.fit(cov_type='cluster', cov_kwds={'groups': df['firm_id']})

# Create relative time dummies
for k in range(-4, 5):
    col = f'rel_{k}' if k >= 0 else f'rel_m{abs(k)}'
    df[col] = (df['relative_time'] == k).astype(int)

# Drop t=-1 as reference
formula = 'y ~ ' + ' + '.join([c for c in rel_cols if c != 'rel_m1']) + ' + C(id) + C(year)'

from linearmodels.iv import IV2SLS

model = IV2SLS.from_formula('y ~ 1 + exog + [endog ~ instrument]', data=df)
results = model.fit(cov_type='clustered', clusters=df['cluster_var'])

Method	Required Checks
DiD	Parallel trends (event study plot), placebo treatment dates
RDD	McCrary density test, bandwidth robustness (half/double IK optimal), polynomial robustness
IV	First-stage F > 10, exclusion restriction argument, over-identification test
Synthetic Control	Pre-treatment RMSPE, placebo distribution, leave-one-out
Matching	Covariate balance table, caliper sensitivity

10-jill0099-causal-inference-mixtape

Summary

SKILL.md

Causal Inference: The Mixtape — Code Skill

Methods Covered

Core Workflow

Implement a Causal Method

Choose the Right Language

Cross-Language Equivalents

Key Python Patterns

DiD with Cluster-Robust SE

Event Study (Lead/Lag)

IV / 2SLS

Robustness Check Patterns

Common Pitfalls

Additional Resources

Reference Files

Prompt Files

Related skills