Elia Lapenta

A Machine-Learning-Compatible Omnibus Test for Treatment Effect Heterogeneity

This study proposes a formal, computationally efficient nonparametric omnibus test for treatment-effect heterogeneity that is compatible with a broad class of estimators, including modern machine-learning methods and is illustrated using two empirical applications on retirement savings and trade liberalization.