causal-inference-mixtape

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.

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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
DAGs / Collider Bias	dagitty (conceptual)	dagitty/ggdag	—	references/method-patterns.md §9
Randomization Inference	permutation loop	ri2	ritest	references/method-patterns.md §10

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Core Workflow

Implement a Causal Method

1. Identify the method from the table above
2. Load the appropriate template from references/method-patterns.md
3. Adapt variable names, fixed effects, and clustering to the user's data
4. Add robustness checks (parallel trends for DiD, McCrary for RDD, first-stage F for IV)

Choose the Right Language

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

Cross-Language Equivalents

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

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Key Python Patterns

DiD with Cluster-Robust SE

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']})

Event Study (Lead/Lag)

# 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)'

IV / 2SLS

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'])

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Robustness Check Patterns

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

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Common Pitfalls

1. TWFE with staggered treatment — standard two-way FE is biased when treatment timing varies. Use Bacon decomposition or Sun & Abraham / Callaway & Sant'Anna estimators.
2. Synthetic Control with many treated units — the Synth package handles one treated unit. For multiple, use augmented synthetic control or stacked approach.
3. RDD without McCrary test — always test for manipulation at the cutoff before estimating.
4. IV weak instruments — report first-stage F-statistic. Below 10 indicates weak instrument bias.
5. Python Synth gap — no mature Python Synth package exists. Use rpy2 to call R's Synth from Python.

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Additional Resources

Reference Files

• references/method-patterns.md — Detailed code templates for all 10 methods with full examples
• references/r-stata-comparison.md — Cross-language package comparison and method coverage gaps

Prompt Files

• prompts/01-implement-method.md — Copy-paste prompt for implementing any causal method
• prompts/02-robustness-checks.md — Copy-paste prompt for generating robustness check code