cross-sectional data

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

Introduction to Causal Inference: Double Machine Learning

Estimating the causal effect of a cash bonus on unemployment duration using Double Machine Learning with the Pennsylvania Bonus Experiment

Introduction to Machine Learning: Random Forest Regression

A beginner-friendly, comprehensive introduction to Random Forest regression for continuous data, evaluated end-to-end with 5-fold cross-validation and out-of-fold predictions on Bolivian satellite imagery

Exploratory Spatial Data Analysis (ESDA)

An interactive geocomputational notebook to study spatial clusters and outliers

Studying spatial heterogeneity

A geocomputational notebook to study spatial heterogeneity using the GWR and MGWR frameworks.

Construct and export spatial connectivity structures (W)

An introduction to how to construct, explore, and export spatial connectivity structures (W) using Python.

Cross-Sectional Spatial Regression in Stata: Crime in Columbus Neighborhoods

Explore the full taxonomy of cross-sectional spatial models --- OLS, SAR, SEM, SLX, SDM, SDEM, SAC, and GNS --- using the Columbus crime dataset in Stata, following Elhorst (2014)

Spatial inequality dynamics

A geocomputational notebook to monitor spatial inequality dynamics.

Introduction to spatial data science

Measuring the evolution of spatial dependence and spatial inequality