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.
| Subjects: | Econometrics (econ.EM) ; Applications (stat.AP); Methodology (stat.ME) |
| Cite as: | arXiv:2108.09400 [econ.EM] |
| (or arXiv:2108.09400v2 [econ.EM] for this version) | |
| https://doi.org/10.48550/arXiv.2108.09400 |
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arXiv-issued DOI via DataCiteFrom: Matias Cattaneo [view email]
[v1] Fri, 20 Aug 2021 23:23:09 UTC (70 KB)
[v2] Thu, 24 Feb 2022 14:22:50 UTC (73 KB)
View a PDF of the paper titled Regression Discontinuity Designs, by Matias D. Cattaneo and Rocio Titiunik