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.
A beginner-friendly walkthrough of Double LASSO for causal inference, replicating Fitzgerald, Lattimore, Robinson and Zhu's (2026) analysis of the Donohue–Levitt abortion–crime question with 284 candidate controls and state-clustered standard errors.
When the 'treatment' is a point in space, distance becomes the running variable. We walk through the parametric ring DiD and a data-driven nonparametric alternative, first on a simulated world with a known answer, then on Linden and Rockoff's home-prices study, and reconcile a parametric −5.78 % with a nonparametric −20.6 %.
An introduction to regional impact evaluation using modern causal-inference methods with worked examples and publicly available data for full reproducibility.
A case study on the Affordable Care Act's Medicaid expansion --- working through 2x2 cell-means, TWFE, covariate-adjusted DRDID, 2xT and Callaway-Sant'Anna staggered event studies, and HonestDiD sensitivity --- to show how population weighting changes the target parameter when the units are regions of very different sizes.
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.
A beginner-friendly tutorial on the synthetic control method in R, using the Basque Country case study to estimate the economic cost of conflict on regional GDP per capita from 1970 to 1997.
Manual demeaning vs two-way fixed effects --- showing that TWFE is just OLS on demeaned data through the Frisch-Waugh-Lovell theorem, with a hands-on proof using a Barro convergence panel of 150 countries.
Dynamic panel Bayesian Model Averaging with the Bayesian Dynamic Systems Modeling (BDSM) R package, applied to cross-country economic growth determinants --- handling reverse causality through lagged dependent variables, fixed effects, and weak exogeneity.