Machine Learning for Causal Discovery in Observational Studies

Abstract

Causal discovery from observational data is a central challenge in empirical social science. We benchmark six constraint-based and score-based algorithms on synthetic and real-world datasets, finding that hybrid approaches consistently outperform pure constraint methods under latent confounding.