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