causal-inference-mixtape

name	causal-inference-mixtape
description	This skill should be used when the user asks to "implement a DiD regression", "write a causal inference pipeline", "set up an event study", "implement instrumental variables", "run a regression discontinuity design", "build a synthetic control model", "implement propensity score matching", "write parallel trends test", "implement Bacon decomposition", or needs code templates for causal inference methods in Python, R, or Stata. Based on Scott Cunningham's Causal Inference: The Mixtape.
version	1.0.0

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