Python

Exploratory Spatial Data Analysis: Spatial Clusters and Dynamics of Human Development in South America

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

Multiscale Geographically Weighted Regression: Spatially Varying Economic Convergence in Indonesia

Applying Multiscale Geographically Weighted Regression (MGWR) to reveal how economic catching-up varies across Indonesia's 514 districts, with each variable operating at its own spatial scale

Synthetic Control with Prediction Intervals: Quantifying Uncertainty in Germany's Reunification Impact

Synthetic control with prediction intervals quantifies uncertainty in Germany's reunification GDP impact using the scpi package.

Introduction to PCA Analysis for Building Development Indicators

Building a composite Health Index from Life Expectancy and Infant Mortality using manual PCA with simulated data for 50 countries, then verifying against scikit-learn

Pooled PCA for Building Development Indicators Across Time

Building a comparable Human Development Index across two time periods using pooled PCA with real sub-national data for 153 South American regions, and contrasting with per-period PCA to show why pooled standardization is essential for temporal comparisons

High-Dimensional Fixed Effects Regression: An Introduction in Python

Estimating regression models with high-dimensional fixed effects using PyFixest, from simple OLS through two-way FE, instrumental variables, panel data, and event studies

Introduction to Difference-in-Differences in Python

Estimating causal treatment effects using Difference-in-Differences with the diff-diff package, from the classic 2x2 design through staggered adoption with Callaway-Sant'Anna and HonestDiD sensitivity analysis

The FWL Theorem: Making Multivariate Regressions Intuitive

Understanding the Frisch-Waugh-Lovell theorem to isolate causal relationships by partialling-out confounders in a simulated retail store dataset

Introduction to Partial Identification: Bounding Causal Effects Under Unmeasured Confounding

Computing causal bounds under unmeasured confounding using Manski and Tian-Pearl bounds with the CausalBoundingEngine package in Python

Introduction to Causal Inference: The DoWhy Approach with the Lalonde Dataset

Estimating the causal effect of a job training program on earnings using DoWhy's four-step causal inference framework with the Lalonde dataset