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.
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.
Learn Difference-in-Differences (DiD) in Python using PyFixest and Great Tables. Covers the 2x2 design, TWFE regression, inference comparison, publication-quality tables, event studies, and parallel trends testing based on Corral and Yang (2024).