causal-inference-methods

# Causal Inference Methods Skill Apply advanced econometric and statistical methods for causal identification in observational data. ## Overview The Causal Inference Methods skill enables application of propensity score methods, instrumental variables, difference-in-differences, regression discontinuity designs, and other quasi-experimental approaches for causal identification in observational social science data. ## Capabilities ### Propensity Score Methods - Score estimation - Matching algorithms - Inverse probability weighting - Doubly robust estimation - Balance assessment ### Instrumental Variables - Instrument identification - Relevance testing - Exclusion restriction - Two-stage estimation - Weak instrument diagnosis ### Difference-in-Differences - Parallel trends assessment - Treatment effect estimation - Staggered adoption designs - Heterogeneous effects - Robustness checks ### Regression Discontinuity - Sharp RD design - Fuzzy RD design - Bandwidth selection - Local polynomial estimation - Validity testing ### Design Considerations - Identification strategy - Assumption testing - Sensitivity analysis - Effect heterogeneity - Interpretation limits ## Usage Guidelines ### When to Use - Estimating causal effects - Evaluating interventions - Analyzing policy impacts - Exploiting natural experiments - Addressing selection bias ### Best Practices - Justify identification strategy - Test assumptions - Report sensitivity analyses - Acknowledge limitations - Pre-...