Author

Pedro Vergara Merino

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Preprint Jul 2026

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.

Elia Lapenta, Anthony Strittmatter, Pedro Vergara Merino · 0 citations