a5c-ai 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