Heterogeneous Treatment Effects in Regression Discontinuity Designs

• URL: http://arxiv.org/abs/2106.11640v1
• Date: Tue, 22 Jun 2021 09:47:28 GMT
• Title: Heterogeneous Treatment Effects in Regression Discontinuity Designs
• Authors: \'Agoston Reguly
• Abstract summary: The paper proposes a supervised machine learning algorithm to uncover treatment effect heterogeneity in classical regression discontinuity designs. I study the performance of the method through Monte Carlo simulations and apply it to the data set compiled by Pop-Eleches and Urquiola (2013) to uncover various sources of heterogeneity in the impact of attending a better secondary school in Romania.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The paper proposes a supervised machine learning algorithm to uncover treatment effect heterogeneity in classical regression discontinuity (RD) designs. Extending Athey and Imbens (2016), I develop a criterion for building an honest ``regression discontinuity tree'', where each leaf of the tree contains the RD estimate of a treatment (assigned by a common cutoff rule) 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. I study the performance of the method through Monte Carlo simulations and apply it to the data set compiled by Pop-Eleches and Urquiola (2013) to uncover various sources of heterogeneity in the impact of attending a better secondary school in Romania.

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