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.

Methods Covered

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

Cross-Language Equivalents

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'])

Robustness Check Patterns

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.

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