Causal Inference Mixtape
Should be used when the user asks to "implement a DiD regression", "write a causal...
Install
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-inference-mixtape -g -a claude-code -y- Repository
- brycewang-stanford/Auto-Empirical-Research-Skills4.9k
- Category
- Design
- Updated
- 6d ago
- License
- See repository
- Context cost
- Lightabout 1,348 tokens
- Runs scripts
- No, instructions only
What it does
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.
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 |
| DAGs / Collider Bias | dagitty (conceptual) | dagitty/ggdag | , | references/method-patterns.md §9 |
| Randomization Inference | permutation loop | ri2 | ritest | references/method-patterns.md §10 |
Core Workflow
Implement a Causal Method
- Identify the method from the table above
- Load the appropriate template from
references/method-patterns.md - Adapt variable names, fixed effects, and clustering to the user's data
- Add robustness checks (parallel trends for DiD, McCrary for RDD, first-stage F for IV)
