Discovering Heterogeneous Treatment Effects in Regression Discontinuity Designs

2021/06/22 by Ágoston Reguly, Reguly, Ágoston
Mathematics · #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (stat.ML) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.2106.11640

openalex publication_date 2021/06/22 · openalex created_date 2021/08/02 · openalex updated_date 2026/07/28

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.

Related

• Treatment Effect Heterogeneity in Regression Discontinuity Designs
• A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs
• Regression Discontinuity Design with Multiple Groups for Heterogeneous Causal Effect Estimation
• A Bayesian Nonparametric Hypothesis Testing Approach for Regression Discontinuity Designs
• Regression Discontinuity Design with Many Thresholds