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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 discon
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill 10-Jill0099-causal-inference-mixtape -a cursor -y
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.
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.
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.
| 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 |
references/method-patterns.md| 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 |
rpy2 to call R's Synth from Python.references/method-patterns.md — Detailed code templates for all 10 methods with full examplesreferences/r-stata-comparison.md — Cross-language package comparison and method coverage gapsprompts/01-implement-method.md — Copy-paste prompt for implementing any causal methodprompts/02-robustness-checks.md — Copy-paste prompt for generating robustness check code