machine learning

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.