A Bayesian Nonparametric Hypothesis Testing Approach for Regression Discontinuity Designs
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.
Cite
@article{arxiv.1402.1909,
title = {A Bayesian Nonparametric Hypothesis Testing Approach for Regression Discontinuity Designs},
author = {George Karabatsos and Stephen G. Walker},
journal= {arXiv preprint arXiv:1402.1909},
year = {2014}
}