Regression Discontinuity Designs

TL;DR

This paper reviews the extensive methodological developments in Regression Discontinuity (RD) designs, covering identification, estimation, inference, and validation within two main analytical frameworks, aiding researchers in causal inference and program evaluation.

Contribution

It provides a comprehensive, organized review of RD methodological literature, highlighting key frameworks, methods, and validation approaches for applied researchers.

Findings

Two main frameworks: continuity and local randomization.

Popular estimation methods include local polynomial regression.

Validation techniques help ensure RD design credibility.

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.