synthetic-control
CommunityData-driven counterfactuals for policy impact.
Education & Research#causal-inference#scm#policy-evaluation#synthetic-control#donor-pool#placebo-tests#synthetic-did
Authorsheehe
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This skill enables rigorous construction and interpretation of synthetic controls to estimate counterfactual outcomes for a treated unit, reducing reliance on parallel-trends assumptions and enabling transparent policy evaluation.
Core Features & Use Cases
- Donor pool construction and selection to best approximate pre-treatment trajectories.
- Weight optimization, gap estimation, and placebo-based inference (in-space and in-time).
- Extensions including augmented SCM and synthetic DID for multiple or staggered treatments.
Quick Start
Create your SCM by providing your panel data with a treated unit, a donor pool, predictor variables, and the pre-treatment period, and run the SCM workflow to estimate the treatment effect.
Dependency Matrix
Required Modules
None requiredComponents
references
💻 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: synthetic-control Download link: https://github.com/sheehe/coase/archive/main.zip#synthetic-control Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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