scientific-causal-inference
CommunityBuild robust causal inference pipelines from data
Data & Analytics#causal-inference#difference-in-differences#observational-data#instrumental-variables#regression-discontinuity#propensity-score-matching#inverse-probability-weighting
Authornahisaho
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Observational data often cannot support randomized experiments, yet estimating causal effects is essential for credible decision-making. This skill provides a modular pipeline of methods to estimate treatment effects under confounding, enabling causal interpretation.
Core Features & Use Cases
- Propensity score matching (PSM) to balance treated and control groups.
- Inverse probability weighting (IPW/IPTW), instrumental variables, difference-in-differences (DID), regression discontinuity design (RDD), and DAG-based confounder selection.
- Use cases span epidemiology, economics, and social sciences, with templates and analyses for common observational-study scenarios.
Quick Start
Provide a dataset with treatment, outcome, and covariates, then run the causal-inference pipeline to obtain the estimated effects.
Dependency Matrix
Required Modules
None requiredComponents
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: scientific-causal-inference Download link: https://github.com/nahisaho/satori/archive/main.zip#scientific-causal-inference Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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