byjames-traina· 20 skills
This skill covers causal inference methods in observational and quasi-experimental settings. Use when the user is implementing, choosing between, or debugging causal identification strategies — including instrumental variables, difference-in-differences, regression discontinuity, synthetic control, or matching estimators. Triggers on "causal effect", "identification strategy", "instrumental variable", "2SLS", "GMM", "difference-in-differences", "DiD", "staggered treatment", "regression discontinuity", "RDD", "synthetic control", "matching", "propensity score", "IPW", "AIPW", "doubly robust", "LATE", "ATT", "ATE", "parallel trends", "exclusion restriction", "first stage", "weak instruments", or "endogeneity".
$npx -y skills add james-traina/compound-science --skill causal-inferenceInstalls into the current project.
Run `npx skills use "https://github.com/james-traina/compound-science" --skill "james-traina/compound-science/causal-inference"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/james-traina/compound-science" that are relevant to the current task. Run `npx skills add "https://github.com/james-traina/compound-science"` and select the relevant skills, then follow their instructions.