Regression Discontinuity Designs

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.

Automated summary

This article provides a curated review of the methodological literature on regression discontinuity design, focusing on the continuity framework and the local randomization framework. It discusses designs and parameters, estimation and inference methods, and validation and falsification approaches.