Covariate Adjustment in Regression Discontinuity Designs

TL;DR

This paper reviews the role of covariate adjustment in Regression Discontinuity designs, clarifying misconceptions and providing guidance for correct application in causal inference and program evaluation.

Contribution

It offers a comprehensive review and methodological guidance on covariate adjustment in RD designs, addressing common misconceptions and clarifying its proper use.

Findings

Clarifies the role of covariate adjustment in RD analysis

Provides methodological guidance for correct covariate use

Addresses misconceptions about covariate adjustment

Abstract

The Regression Discontinuity (RD) design is a widely used non-experimental method for causal inference and program evaluation. While its canonical formulation only requires a score and an outcome variable, it is common in empirical work to encounter RD analyses where additional variables are used for adjustment. This practice has led to misconceptions about the role of covariate adjustment in RD analysis, from both methodological and empirical perspectives. In this chapter, we review the different roles of covariate adjustment in RD designs, and offer methodological guidance for its correct use.