Regression Discontinuity Designs

Matias Cattaneo and Rocio Titiunik

Papers from arXiv.org

Abstract: The Regression Discontinuity (RD) design is one of the most widely used non-experimental methods for causal inference and program evaluation. Over the last two decades, statistical and econometric methods for RD analysis have expanded and matured, and there is now a large number of methodological results for RD identification, estimation, inference, and validation. We offer a curated review of this methodological literature organized around the two most popular frameworks for the analysis and interpretation of RD designs: the continuity framework and the local randomization framework. For each framework, we discuss three main topics: (i) designs and parameters, which focuses on different types of RD settings and treatment effects of interest; (ii) estimation and inference, which presents the most popular methods based on local polynomial regression and analysis of experiments, as well as refinements, extensions, and alternatives; and (iii) validation and falsification, which summarizes an array of mostly empirical approaches to support the validity of RD designs in practice.

Date: 2021-08, Revised 2022-02
New Economics Papers: this item is included in nep-ecm and nep-isf
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Citations: View citations in EconPapers (36)

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http://arxiv.org/pdf/2108.09400 Latest version (application/pdf)

Related works:
Journal Article: Regression Discontinuity Designs (2022)
Working Paper: The Regression Discontinuity Design (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2108.09400

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