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504 – The Bootstrap Method for Variance Estimation of the Complex Survey Data
A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs
George Karabatsos
University of Illinois-Chicago
Stephen G. Walker
University of Kent
The regression discontinuity (RD) non-randomized design can identify and estimate causal effects for a "locally-randomized" subgroup of subjects, under relatively mild conditions. Though, the accurate estimation of causal effects still relies on the predictive accuracy of the statistical model. We propose a flexible Bayesian nonparametric regression model which can provide predictively-accurate estimates of causal effects, either in terms of the mean, variance, distribution function, quantile, probability density, or any other functional of the outcome variable. We illustrate the model through the analysis of two real educational sets.