Skip to content

README-original.md

Latest commit

 

History

History
135 lines (96 loc) · 4.56 KB

README-original.md

File metadata and controls

135 lines (96 loc) · 4.56 KB
Raw

Causal Inference: The Mixtape — Claude Code Skill

A Claude Code skill providing ready-to-run code templates for causal inference methods, built from Scott Cunningham's Causal Inference: The Mixtape repository.

Languages: Python · R · Stata

What It Does

This skill helps you:

  1. Implement causal inference methods — DiD, RDD, IV, Synthetic Control, Matching, and more
  2. Choose the right language — cross-language equivalents and coverage gap analysis
  3. Write robustness checks — parallel trends, McCrary tests, Bacon decomposition, bandwidth robustness
  4. Avoid common pitfalls — staggered DiD bias, weak instruments, missing diagnostics

Methods Covered (10)

Method Python R Stata
OLS / Regression statsmodels estimatr reg/reghdfe
Difference-in-Differences statsmodels lfe/fixest reghdfe
Event Study (Dynamic DiD) manual lead/lag fixest (sunab) reghdfe + coefplot
Staggered DiD / TWFE statsmodels bacondecomp / did bacondecomp / csdid
Regression Discontinuity statsmodels rdrobust rdrobust
Instrumental Variables linearmodels IV2SLS AER/ivreg ivregress 2sls
Synthetic Control rpy2 → R Synth Synth + SCtools synth
Matching / PSM / IPW manual logit + weights MatchIt + ipw teffects / cem
DAGs / Collider Bias conceptual dagitty + ggdag —
Randomization Inference permutation loop ri2 ritest

Trigger Phrases

Say any of the following to activate this skill:

  • 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
  • implement Bacon decomposition

Installation

Copy the skill folder to your Claude Code skills directory:

cp -r causal-inference-mixtape ~/.claude/skills/

Or clone directly:

git clone https://github.com/Jill0099/causal-inference-mixtape.git ~/.claude/skills/causal-inference-mixtape

File Structure

causal-inference-mixtape/
├── SKILL.md                              # Core skill (auto-loaded when triggered)
├── references/
│   ├── method-patterns.md               # Full code templates for all 10 methods
│   └── r-stata-comparison.md            # Cross-language coverage gaps & packages
└── prompts/
    ├── 01-implement-method.md           # Copy-paste: implement any causal method
    └── 02-robustness-checks.md          # Copy-paste: DiD/RDD/IV robustness code

Key Features

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', ...) felm(y ~ x | 0 | 0 | cl) reg y x, cluster(id)
Two-way FE C(id) + C(time) felm(y ~ x | id + time) reghdfe y x, absorb(id time)
IV / 2SLS IV2SLS.from_formula(...) ivreg(y ~ exog | inst) ivregress 2sls y (endog = inst)

Python Gaps Documented

Some methods lack mature Python implementations:

  • Synthetic Control → use rpy2 to call R's Synth
  • Bacon Decomposition → use R (bacondecomp) or Stata
  • Coarsened Exact Matching → use Stata (cem) or R (MatchIt)
  • McCrary Density Test → use R (rdd)

Robustness Check Patterns

Method Required Checks
DiD Parallel trends (event study plot), placebo treatment dates
RDD McCrary density test, bandwidth robustness, polynomial robustness
IV First-stage F > 10, exclusion restriction, over-identification test
Synthetic Control Pre-treatment RMSPE, placebo distribution, leave-one-out
Matching Covariate balance table, caliper sensitivity

Prompts (Copy-Paste Ready)

The prompts/ folder contains standalone prompts for use without Claude Code:

File Use Case
01-implement-method.md Implement any causal method with diagnostics
02-robustness-checks.md Generate robustness check code for DiD / RDD / IV

Each prompt has fill-in fields — replace with your paper's details and paste into any Claude chat.

Source

Built from systematic analysis of Scott Cunningham's Causal Inference: The Mixtape repository:

  • 58 Python scripts
  • ~56 R scripts
  • ~60 Stata .do files
  • Full course curriculum (9 sections)

License

MIT