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-register when possible

Integration Points

Quantitative Methods skill
Program Evaluation skill
Systematic Review skill
Policy Communication skill

References

Propensity Score Analysis process
Natural Experiment Analysis process
Quasi-Experimental Design process
Causal Inference Analyst agent