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-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