Discovering Heterogeneous Treatment Effects in Regression Discontinuity Designs

Agoston Reguly

Papers from arXiv.org

Abstract: The paper proposes a causal supervised machine learning algorithm to uncover treatment effect heterogeneity in sharp and fuzzy regression discontinuity (RD) designs. We develop a criterion for building an honest ``regression discontinuity tree'', where each leaf contains the RD estimate of a treatment conditional on the values of some pre-treatment covariates. It is a priori unknown which covariates are relevant for capturing treatment effect heterogeneity, and it is the task of the algorithm to discover them, without invalidating inference, while employing a nonparametric estimator with expected MSE optimal bandwidth. We study the performance of the method through Monte Carlo simulations and apply it to uncover various sources of heterogeneity in the impact of attending a better secondary school in Romania.

Date: 2021-06, Revised 2025-08
New Economics Papers: this item is included in nep-big and nep-ecm
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