causal

Six Ways to Evaluate a Policy using R: Comparative Case Studies of Proposition 99

Six estimators in one tutorial --- naive pre-post, DiD, two flavours of ITS, RDD on time, Synthetic Control, and CausalImpact --- all applied to California's 1988 Proposition 99 cigarette tax to see how much (and where) they disagree.

Bayesian Spatial Synthetic Control: California's Proposition 99 in R

Replicating the California tobacco case study from Sakaguchi & Tagawa in R: three estimators, one ATT, and a Nevada-sized spillover.

Do Institutions Cause Prosperity? An IV Tutorial in Python

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.

Do Institutions Cause Prosperity? An IV Tutorial in Stata

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

Causal Machine Learning and the Resource Curse with Python EconML

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

Causal Machine Learning and the Resource Curse with Stata 19

Estimate heterogeneous causal effects of mining and mineral prices on economic development using Stata 19's cate command with multi-valued treatment via pairwise binary comparisons, applied to a simulated resource curse panel dataset

A Beginner's Guide to Causal Inference with DoWhy in Python

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

Double Machine Learning with 401(k) Data: From Eligibility Effects to Complier Analysis

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

MGWFER: Causal Spatially Varying Coefficients via Panel Fixed Effects

A faithful Python tutorial on Li & Fotheringham (2026) — using a two-stage MGWFER algorithm to remove time-invariant spatial confounders from Multiscale GWR and recover both unbiased spatially varying slopes and intrinsic contextual effects from simulated panel data (225 units x 3 periods).

Causal Machine Learning for Policy Evaluation: From ATE to IATE to a Better Assignment Rule

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.