How To Use The Synthetic Control Method in R Step-By-Step: Effect of California's Tobacco Program

In this video, we learn to use a synthetic control to estimate the effect of California's Tobacco Control program, replicating the seminal paper by Abadie, Diamond, & Hainmueller (2010). GitHub Repository: https://github.com/causalify-code/synthetic-control-replications/tree/main/california-tobacco-control-program References: Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American statistical Association, 105(490), 493-505. 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). Abadie, A., & Gardeazabal, J. (2003). The economic costs of conflict: A case study of the Basque Country. American economic review, 93(1), 113-132. Abadie, A., Diamond, A., & Hainmueller, J. (2015). Comparative politics and the synthetic control method. American Journal of Political Science, 59(2), 495-510. Alves, M. F. (2022). Causal Inference for The Brave and True. Retrieved from https://matheusfacure.github.io/python-causality-handbook/landing-page.html Timestamps 00:00 Tutorial Purpose 00:26 Method History 00:54 White Paper Introduction 02:00 Synth R Package Introduction 02:35 R Studio Setup 03:30 Synth Package Setup 04:12 Load Dataset 07:30 Paper Review & Concepts 16:27 Dataprep Parameters 33:00 Dataprep Output 44:50 Synth Function 48:08 Synthetic Control Plot 52:44 Gaps Plot 56:32 Tutorial Recap & Next Steps 01:01:43 Closing Remarks Correction: 01:00:42 This is a mistake. Evidently, the placebo in time figure shows a close match between Synthetic West Germany and West Germany up until 1990. Figure time range is 1960-1990.