Analyzing Regression-Discontinuity Designs With Multiple Assignment Variables: A Comparative Study of Four Estimation Methods. (April 2013)

• Record Type: Journal Article
• Authors: Wong, Vivian C.; Steiner, Peter M.; Cook, Thomas D.
• Is Part Of: Journal of educational and behavioral statistics. Volume 38:Number 2(2013)
• Page Start: 107
• Page End: 141
• Publication Date: 2013-04
• Subjects: regression-discontinuity -- causal inference -- evaluation; Educational statistics -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 370.2
• DOI: 10.3102/1076998611432172
• Languages: English
• ISSNs: 1076-9986

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.