Regression Discontinuity Designs
Matias Cattaneo () and
Papers from arXiv.org
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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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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