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arXiv · 1402.1909

A Bayesian Nonparametric Hypothesis Testing Approach for Regression Discontinuity Designs

George Karabatsos·Stephen G. Walker
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Abstract

The regression discontinuity (RD) design is a popular approach to causal inference in non-randomized studies. This is because it can be used to identify and estimate causal effects under mild conditions. Specifically, for each subject, the RD design assigns a treatment or non-treatment, depending on whether or not an observed value of an assignment variable exceeds a fixed and known cutoff value. In this paper, we propose a Bayesian nonparametric regression modeling approach to RD designs, which exploits a local randomization feature. In this approach, the assignment variable is treated as a covariate, and a scalar-valued confounding variable is treated as a dependent variable (which may be a multivariate confounder score). Then, over the model's posterior distribution of locally-randomized subjects that cluster around the cutoff of the assignment variable, inference for causal effects are made within this random cluster, via two-group statistical comparisons of treatment outcomes and non-treatment outcomes. We illustrate the Bayesian nonparametric approach through the analysis of a real educational data set, to investigate the causal link between basic skills and teaching ability.

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George Karabatsos, Stephen G. Walker. 2014-02-09. A Bayesian Nonparametric Hypothesis Testing Approach for Regression Discontinuity Designs. https://arxiv.org/abs/1402.1909

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