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synthetic-control

Construct synthetic controls to estimate counterfactual outcomes for treated units.

Updated Apr 15, 2026
Search Tags:#causal-inference#scm#policy-evaluation#synthetic-control#donor-pool#placebo-tests#synthetic-did
One-click install
npx skills add https://github.com/sheehe/coase --skill synthetic-control
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: synthetic-control
Source: https://github.com/sheehe/coase/tree/main/%E5%AE%9E%E8%AF%81%E7%A7%91%E7%A0%94%E6%8F%92%E4%BB%B6/econometrics/econometrics/skills/synthetic-control
Command: npx skills add https://github.com/sheehe/coase --skill synthetic-control

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

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.

Frequently Asked Questions about synthetic-control

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I construct a synthetic control to estimate policy impact with a single treated unit?▼

To construct a synthetic control, you provide panel data with a treated unit, a donor pool, predictor variables, and the pre-treatment period to optimize weights and estimate the counterfactual outcome for policy evaluation.

What is synthetic control method inference and how do placebo tests work?▼

Synthetic control inference uses placebo tests, both in-space and in-time, alongside diagnostics like pre-treatment RMSPE, gap plots, and MSPE ratios to validate the estimated treatment effect against counterfactual outcomes.

Can I use synthetic control methods for multiple or staggered treatment timings?▼

Yes, synthetic control extensions like augmented SCM and synthetic DID support policy evaluation scenarios involving multiple or staggered treatments across different units and time periods.

What diagnostics do I need to verify a credible pre-treatment fit for synthetic control?▼

You need to check pre-treatment RMSPE, review gap plots, analyze placebo plots, and calculate MSPE ratios to ensure the donor pool weights provide a credible pre-treatment fit for the synthetic control.

When should I use synthetic control instead of difference-in-differences for policy evaluation?▼

Use synthetic control when parallel-trends assumptions are unreliable, as it constructs a data-driven counterfactual from a weighted donor pool to estimate policy impact transparently with few treated units.

How do I select the donor pool and predictors for a synthetic control estimation?▼

Select donor pool units and predictor variables that best approximate the treated unit's pre-treatment trajectories, ensuring the weighted combination produces a credible counterfactual outcome for gap estimation.

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