Corpus ID: 237563270

Regression Discontinuity Design with Potentially Many Covariates

@inproceedings{Arai2021RegressionDD,
  title={Regression Discontinuity Design with Potentially Many Covariates},
  author={Yoichi Arai and Taisuke Otsu and Myung Hwan Seo},
  year={2021}
}
Abstract. This paper studies the case of possibly high-dimensional covariates in the regression discontinuity design (RDD) analysis. In particular, we propose estimation and inference methods for the RDD models with covariate selection which perform stably regardless of the number of covariates. The proposed methods combine the local approach using kernel weights with l1-penalization to handle high-dimensional covariates, and the combination is new in the literature. We provide theoretical and… Expand

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