Causal Inference

Do industrial parks raise local economic activity — and for whom? A beginner's staggered difference-in-differences evaluation of Ethiopian industrial parks in Python, replicating Huang, Wang & Xu (2026) on synthetic calibrated data: TWFE and an event study with pyfixest, the modern Sun-Abraham, Borusyak/Gardner and Callaway-Sant'Anna estimators plus a Goodman-Bacon decomposition with diff-diff, survey-weighted repeated-cross-section DiD on DHS household welfare and women's empowerment, and Conley spatial standard errors.

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.

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.

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.

Python companion to the R and Stata Double LASSO tutorials — same data, same five estimators, plus a hands-on introduction to the DoubleML library (DoubleMLPLR, DoubleMLIRM, and learner-robustness across LASSO, RandomForest, XGBoost).

Stata companion to the R Double LASSO tutorial — same data, same five estimators, replicating the Belloni-Chernozhukov-Hansen 284-control extension of Donohue and Levitt's abortion-and-crime panel with pdslasso, rlasso, and cvlasso.

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.

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

Estimate heterogeneous causal effects of mining and mineral prices on economic development using EconML's CausalForestDML with Double Machine Learning, applied to simulated resource curse data