Analyzing Regression-Discontinuity Designs With Multiple Assignment Variables

Vivian C. Wong, Peter M. Steiner and Thomas D. Cook
Additional contact information
Vivian C. Wong: Northwestern University
Peter M. Steiner: University of Wisconsin
Thomas D. Cook: Northwestern University

Journal of Educational and Behavioral Statistics, 2013, vol. 38, issue 2, 107-141

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.

Keywords: regression-discontinuity; causal inference; evaluation (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.3102/1076998611432172 (text/html)

Related works:
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:sae:jedbes:v:38:y:2013:i:2:p:107-141

DOI: 10.3102/1076998611432172

Access Statistics for this article

More articles in Journal of Educational and Behavioral Statistics
Bibliographic data for series maintained by SAGE Publications ().