Analyzing Regression-Discontinuity Designs With Multiple Assignment Variables: A Comparative Study of Four Estimation Methods

Vivian C. Wong, Peter M. Steiner, Thomas D. Cook

Research output: Contribution to journalArticle

44 Scopus citations

Abstract

In a traditional regression-discontinuity design (RDD), units are assigned to treatment on the basis of a cutoff score and a continuous assignment variable. The treatment effect is measured at a single cutoff location along the assignment variable. This article introduces the multivariate regression-discontinuity design (MRDD), where multiple assignment variables and cutoffs may be used for treatment assignment. For an MRDD with two assignment variables, we show that the frontier average treatment effect can be decomposed into a weighted average of two univariate RDD effects. The article discusses four methods for estimating MRDD treatment effects and compares their relative performance in a Monte Carlo simulation study under different scenarios.

Original languageEnglish (US)
Pages (from-to)107-141
Number of pages35
JournalJournal of Educational and Behavioral Statistics
Volume38
Issue number2
DOIs
StatePublished - Apr 1 2013

Keywords

  • causal inference
  • evaluation
  • regression-discontinuity

ASJC Scopus subject areas

  • Education
  • Social Sciences (miscellaneous)

Fingerprint Dive into the research topics of 'Analyzing Regression-Discontinuity Designs With Multiple Assignment Variables: A Comparative Study of Four Estimation Methods'. Together they form a unique fingerprint.

  • Cite this