Introduce and derive synthetic difference-in-differences, then apply it to California's Proposition 99 — comparing SDID with the original difference-in-differences and synthetic control (synth2), and how to run placebo inference with a single treated unit.
A hands-on tour of the Augmented Synthetic Control Method in a multi-country setting with the augsynth package — learning single_augsynth, multisynth, and augsynth_multiout on simulated data, then replicating Papaioannou (2021) on the EMU and productivity convergence.
Synthetic Control and IV in Python — replicating Andersson (2019) on Sweden's carbon tax and CO2 emissions with pysyncon and pyfixest.
Six estimators in one tutorial --- naive pre-post, DiD, two flavours of ITS, RDD on time, Synthetic Control, and CausalImpact --- all applied to California's 1988 Proposition 99 cigarette tax to see how much (and where) they disagree.
Replicating the California tobacco case study from Sakaguchi & Tagawa in R: three estimators, one ATT, and a Nevada-sized spillover.
Estimate the causal effect of California's Proposition 99 tobacco control program on cigarette sales using the synthetic control method in Stata, with in-space placebo, in-time placebo, and leave-one-out robustness tests