Build causal inference analyses to estimate intervention effects using templates drawn from Causal Inference: The Mixtape. Adapt code across Python, R, and Stata, with checks for method assumptions and guidance on language-specific packages and limitations.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
실행 점수 미검증
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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 code아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Turn a broad research idea into clear, testable questions. Compare familiar and less conventional approaches, structure the selected question around its population and methods, and assess variables, feasibility, and potential theoretical contributions.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Helps build a research framework by comparing theories, mapping relationships between variables, and deriving testable hypotheses. Explores less conventional options, examines weaknesses and competing explanations, and produces diagrams with anticipated reviewer objections.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Clarify the philosophical assumptions behind a research project and check whether they fit the proposed methods. Get a reasoned research stance, draft proposal wording, and guidance for explaining how the researcher's background and values shape the study.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Plan and draft academic papers through specialized agent roles covering literature, arguments, citations, and review. Get chapter guidance, revision roadmaps, Traditional Chinese and English abstracts, and documents formatted as LaTeX, DOCX, PDF, or Markdown.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Turn a detailed outline into a philosophy or interdisciplinary manuscript. Draft chapters with repeated quality reviews, check terminology and arguments across sections, and prepare the manuscript, evaluation reports, abstract, and metadata for submission.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Simulate academic peer review through five reviewer roles tailored to the paper's field. Receive separate critiques, an editorial decision, and a prioritized revision roadmap, or check whether a revised manuscript addresses earlier comments.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.