python

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.

How persistent is firm employment? Pooled OLS, fixed effects, Anderson-Hsiao IV, Arellano-Bond difference GMM, and Blundell-Bond system GMM on the classic 140-firm UK panel — and how the AR(2), Hansen, and instrument-collapse diagnostics separate the one defensible estimate from four seductive wrong ones.

A Data Science Repository to Study Regional Development across 514 Districts in Indonesia

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.

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).

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

Replicate Acemoglu, Johnson and Robinson (2001) in Python with pyfixest and linearmodels: instrument modern institutions with settler mortality across 64 ex-colonies and learn how IV recovers a causal effect that OLS understates by 80 percent.

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

A beginner-friendly introduction to causal inference using DoWhy's four-step framework with simulated observational data on working from home and productivity

Estimating the causal effect of 401(k) eligibility and participation on net financial assets using three DoubleML models (PLR, IRM, IIVM) with the 1991 SIPP pension dataset