Comparative Causal Metrics

Welcome to Comparative Causal Metrics! (Work in Progress)

An introduction to regional impact evaluation using modern causal-inference methods implemented in R and Quarto. The resource covers quasi-experimental techniques for evaluating policy effects and interventions on regional outcomes, with worked examples and publicly available data for full reproducibility.

This work in progress book features:

• Quasi-experimental Methods — From interrupted time series to synthetic control and Bayesian structural time series, with a regional comparative focus.
• R + Quarto Notebooks — Reproducible chapters with collapsible code, ready to render locally or extend with your own data.

Chapters

1. Introduction
2. Interrupted Time Series
3. Regression Discontinuity in Time
4. Basic Differences-in-Differences
5. Classical Synthetic Control
6. Structural Bayesian Time Series
7. References

Contribute and provide feedback at https://github.com/quarcs-lab/ccm.

Related project

Companion resource: Mastering Causal Metrics — an AI-powered Python study guide based on Angrist & Pischke’s Mastering ‘Metrics.