synthetic-control

Bouncing Back Better? Evaluating the Economic Impact of the Aceh Tsunami

Evaluate the long-run economic impact of a localized natural disaster with causal inference in Python. A beginner's replication of Heger & Neumayer (2019) on the 2004 Aceh tsunami, using synthetic calibrated data: dynamic difference-in-differences with pyfixest, an event study with diff-diff, a night-lights dose-response, synthetic control with mlsynth, and Conley spatial standard errors.

The Augmented Synthetic Control Method: A Beginner's Tutorial with the Kansas Tax Cuts

A beginner-friendly, intuition-first tutorial on the Augmented Synthetic Control Method (ASCM) for a single treated unit — estimating the effect of the 2012 Kansas tax cuts on GDP per capita with the augsynth package, from classic SCM to ridge augmentation, with a careful tour of four ways to do inference.

Staggered Synthetic Difference-in-Differences (SDID) in Stata: Gender Quotas and Women in Parliament

Extend synthetic difference-in-differences to staggered adoption, where units adopt treatment at different times, and apply it in Stata to parliamentary gender quotas across 119 countries — deriving the per-cohort estimator, its aggregation into the overall ATT, the modern sdid_event event study, and bootstrap, jackknife, and placebo inference.

Synthetic Difference-in-Differences (SDID) in Stata: Re-evaluating California's Proposition 99

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.

Augmented Synthetic Control for Multiple Countries: A Tutorial with augsynth

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.

Carbon Taxes and CO2 Emissions: A Synthetic-Control Analysis in Python

Synthetic Control and IV in Python — replicating Andersson (2019) on Sweden's carbon tax and CO2 emissions with pysyncon and pyfixest.

Six Ways to Evaluate a Policy using R: Comparative Case Studies of Proposition 99

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.

Bayesian Spatial Synthetic Control: California's Proposition 99 in R

Replicating the California tobacco case study from Sakaguchi & Tagawa in R: three estimators, one ATT, and a Nevada-sized spillover.

The Synthetic Control Method in Stata: Did California's Tobacco Tax Cut Smoking?

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