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
Using within-transformation to remove time-invariant spatial confounders from Multiscale GWR, recovering unbiased spatially varying coefficients from simulated panel data (225 units x 3 periods)
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.
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).
A beginner-friendly tour of seven panel-data estimators — POLS, Between, First-Differences, Fixed Effects, Two-Way FE, Random Effects, and Correlated Random Effects (Mundlak) — applied to a two-period worker wage panel.
Replicating the N-shaped Kuznets curve with panel data fixed effects in Python using PyFixest, from pooled OLS through two-way FE, turning point analysis, and determinants of regional inequality across 180 countries
Comparing standard error estimators in panel data regressions using Python and linearmodels --- from conventional to clustered, Driscoll-Kraay, and fixed effects
An introduction to exploratory spatial data analysis using PySAL, covering choropleth maps, spatial weights, Moran's I, LISA clusters, space-time dynamics, and a Venezuela-Bolivia comparative analysis for 153 South American regions