synthetic-control

Construct synthetic counterfactuals from donor units to identify causal effects.

6|1|Updated Apr 3, 2026

Search Tags:#causal-inference #panel-data #synthetic-control #donor-pool #synthetic-did #placebo-test #augmented-scm

Ready to run

npx skills add https://github.com/zhouziyue233/great-econometrics --skill synthetic-control-zhouziyue233

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: synthetic-control
Source: https://github.com/zhouziyue233/great-econometrics/tree/main/skills/synthetic-control
Command: npx skills add https://github.com/zhouziyue233/great-econometrics --skill synthetic-control-zhouziyue233

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Causal effects for a single treated unit when randomized experiments are infeasible, achieved by constructing a data-driven counterfactual from donor units.

Core Features & Use Cases

Donor pool construction and weight optimization to match pre-treatment trajectories.
Inference via placebo tests and augmented SCM options (synthetic DID where applicable).
Applications include single-unit treatment, staggered adoption, and multi-unit scenarios for policy evaluation.

Quick Start

Configure your dataset with a treated unit and donor pool, then run the SCM workflow to obtain treatment effects, predictor balance, and placebo diagnostics.

Frequently Asked Questions about synthetic-control

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

FAQPage Schema

How do I estimate causal effects for a single treated unit using synthetic control?▼

Synthetic control estimates causal effects by constructing a data-driven counterfactual from a donor pool. It optimizes donor weights to match the treated unit's pre-treatment trajectories, allowing you to measure policy impact when randomized experiments are infeasible.

Can I apply synthetic control methods to multiple treated units with staggered adoption?▼

Yes, synthetic control supports multi-unit scenarios including staggered adoption and synthetic DID. It constructs counterfactuals for multiple treated units simultaneously, applying placebo-based inference to ensure robust causal effect estimation across different treatment timings.

How do I run placebo tests for synthetic control inference?▼

Placebo tests for synthetic control inference are run by iteratively assigning the treatment status to each donor unit. This generates a distribution of placebo effects, allowing you to benchmark the actual treatment effect against random permutations and calculate pre/post RMSPE.

What diagnostics do I need to validate a synthetic counterfactual?▼

Validating a synthetic counterfactual requires checking predictor balance, donor weights, and pre/post RMSPE. These diagnostics ensure the donor pool accurately mirrors the treated unit's pre-treatment characteristics and measure the causal effect's magnitude and significance.

When should I use augmented synthetic control or synthetic DID instead of standard SCM?▼

Augmented synthetic control or synthetic DID is used when standard SCM struggles with poor pre-treatment fit or staggered adoption. These approaches improve counterfactual construction by incorporating model adjustments, yielding more credible causal effect estimates.