Supporting materials

Strengths
Download slides
Download transcript

Practical considerations
Download slides
Download transcript

Recommended reading

• Abadie, A. (2021). Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects. Journal of Economic Literature, 59(2), 391–425.
• Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235–267.
• Abadie, A., Diamond, A., & Hainmueller, J. (2011). Synth: An R Package for Synthetic Control Methods in Comparative Case Studies. Journal of Statistical Software, 42(13), 1–17. https://doi.org/10.18637/jss.v042.i13
• Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge university press. https://doi.org/10.1017/CBO9781139025751
• Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton university press.
• Xu, Y. (2017). Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models. Political Analysis, 25(1), 57–76.
• Cunningham, S. (2021). Causal Inference: The Mixtape. Yale university press; 2021 Jan 26. https://mixtape.scunning.com/
• Doudchenko, N., & Imbens, G. W. (2016). Balancing, Regression, Difference-in-Differences, and Synthetic Control Methods: A Synthesis. NBER Working Paper No. w22791. https://www.nber.org/papers/w22791
• Robbins, M. W., & Davenport, S. (2021). Microsynth: Synthetic Control Methods for Disaggregated and Micro-Level Data in R. Journal of Statistical Software, 97, 1–31.
• Ben-Michael, E., Feller, A., & Rothstein, J. (2021). The Augmented Synthetic Control Method. Journal of the American Statistical Association, 116(536), 1789–1803.
• Causal Inference for the Brave and True (by Matheus Facure) https://matheusfacure.github.io/python-causality-handbook/