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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (35)
Downloads: (external link)
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) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2108.09400
Access Statistics for this paper
More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().