did-causal
CommunityEstimate causal effects with modern DID
Education & Research#causal inference#econometrics#panel data#difference-in-differences#staggered adoption#parallel trends#ATT estimation
Authorxjtulyc
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Difference-in-differences methods estimate causal treatment effects, but classic estimators can produce biased results under staggered adoption and heterogeneous effects; this Skill helps you compute credible DID estimates while validating key assumptions.
Core Features & Use Cases
- Two-Way Fixed Effects (TWFE) DID: Estimate treatment effects with unit and time fixed effects for panel data.
- Parallel trends pre-testing & event-study plots: Diagnose whether treated and control groups follow similar pre-treatment trends.
- Staggered adoption support (Callaway-Sant'Anna) & bias diagnosis (Goodman-Bacon): Use C&S ATT(g,t) for heterogeneous staggered rollout and use Bacon decomposition to understand TWFE bias patterns.
Quick Start
Use the did-causal skill to estimate the causal impact of a policy change on an outcome using a panel dataset, including a parallel trends pre-test and (if treatment timing is staggered) a Callaway-Sant'Anna estimator.
Dependency Matrix
Required Modules
linearmodelspandasnumpymatplotlibstatsmodelsdidbacondecompfixestdplyrggplot2
Components
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: did-causal Download link: https://github.com/xjtulyc/awesome-rosetta-skills/archive/main.zip#did-causal Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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