A careful introduction to mlsynth, the Python library that puts the whole family of single-treated-unit synthetic control estimators behind one configuration interface. We climb the ladder from difference-in-differences to synthetic difference-in-differences with one mlsynth class per stage, showing what every option does and where the defaults will quietly hand you a different estimator. The case study is the 2016 Brexit referendum and what it cost UK GDP.
Climbing the ladder from difference-in-differences to synthetic difference-in-differences, one stage at a time, with every estimator hand-coded before it is run with its package. The case study is the 2016 Brexit referendum and what it cost UK GDP. Includes cheat sheets in R, Stata and Python.
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.
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.
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 walk-through of Causal Machine Learning — ATE, GATE, IATE, and welfare-maximising assignment — using DoubleML and EconML on a synthetic Flanders ALMP-style cohort with known true effects.
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