TY - JOUR
T1 - Regression Discontinuity Designs
AU - Cattaneo, Matias D.
AU - Titiunik, Rocio
N1 - Funding Information:
A preliminary version of this article was titled “Regression discontinuity designs: a review.” We are indebted to our collaborators Sebastian Calonico, Max Farrell, Yingjie Feng, Brigham Frandsen, Nicolas Idrobo, Michael Jansson, Luke Keele, Xinwei Ma, Kenichi Nagasawa, Jasjeet Sekhon, and Gonzalo Vazquez-Bare for their continued support and intellectual contribution to our research program on regression discontinuity designs. We also thank Jason Lindo, Filippo Palomba, Zhuan Pei, and a reviewer for valuable comments. Both M.D.C. and R.T. gratefully acknowledge financial support from the National Science Foundation through grants SES-1357561 and SES-2019432, and M.D.C. also gratefully acknowledges financial support from the National Institute of Health (R01 GM072611-16). Parts of this article were presented at the Summer Institute 2021 Methods Lectures of the National Bureau of Economic Research (a video recording is available at https://www.nber.org/conferences/si-2021-methods-lecture-causal-inference-using-synthetic-controls-and-regression-discontinuity). General purpose software, replication files, additional articles on regression discontinuity methodology, and other resources are available at https://rdpackages.github.io/.
Publisher Copyright:
© 2022 Annual Reviews Inc.. All rights reserved.
PY - 2022
Y1 - 2022
N2 - The regression discontinuity (RD) design is one of the most widely used nonexperimental 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: (a) designs and parameters, focusing on different types of RD settings and treatment effects of interest; (b) estimation and inference, presenting the most popular methods based on local polynomial regression and methods for the analysis of experiments, as well as refinements, extensions, and alternatives; and (c) validation and falsification, summarizing an array of mostly empirical approaches to support the validity of RD designs in practice.
AB - The regression discontinuity (RD) design is one of the most widely used nonexperimental 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: (a) designs and parameters, focusing on different types of RD settings and treatment effects of interest; (b) estimation and inference, presenting the most popular methods based on local polynomial regression and methods for the analysis of experiments, as well as refinements, extensions, and alternatives; and (c) validation and falsification, summarizing an array of mostly empirical approaches to support the validity of RD designs in practice.
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U2 - 10.1146/annurev-economics-051520-021409
DO - 10.1146/annurev-economics-051520-021409
M3 - Review article
AN - SCOPUS:85118063044
SN - 1941-1383
VL - 14
SP - 821
EP - 851
JO - Annual Review of Economics
JF - Annual Review of Economics
ER -