machine learning

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

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

Conditional Average Treatment Effects (CATE) with Stata 19

Estimate how the effect of 401(k) eligibility on household assets varies across households using Stata 19's new cate command, with PO, AIPW, GATE, GATES, and nonparametric series estimators applied to the canonical assets3 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

Predicting municipal development in Bolivia using Random Forest regression on satellite image embeddings

Mapping the dimensions of poverty through big data, socioeconomic surveys and machine learning in Cambodia

We use new big data sources, the Cambodia Socio-Economic Survey, and machine learning methods to predict and map multidimensional poverty in Cambodia.

Lack of Global Convergence and the Formation of Multiple Welfare Clubs across Countries: An Unsupervised Machine Learning Approach

The paper incorporates some recent developments from the unsupervised machine learning literature to re-evaluate the cross-country convergence hypothesis in a context beyond GDP. The application of a distribution-based clustering algorithm suggests the formation of three local convergence clubs.