Journal Article

Bayesian model averaging for nonparametric discontinuity design

Leeftink, David, van Gerven, Marcel A. J., Hinne, Max, Ambrogioni, Luca

2022

Overview

Quasi-experimental research designs, such as regression discontinuity and interrupted time series, allow for causal inference in the absence of a randomized controlled trial, at the cost of additional assumptions. In this paper, we provide a framework for discontinuity-based designs using Bayesian model averaging and Gaussian process regression, which we refer to as ‘Bayesian nonparametric discontinuity design’, or BNDD for short. BNDD addresses the two major shortcomings in most implementations of such designs: overconfidence due to implicit conditioning on the alleged effect, and model misspecification due to reliance on overly simplistic regression models. With the appropriate Gaussian process covariance function, our approach can detect discontinuities of any order, and in spectral features. We demonstrate the usage of BNDD in simulations, and apply the framework to determine the effect of running for political positions on longevity, of the effect of an alleged historical phantom border in the Netherlands on Dutch voting behaviour, and of Kundalini Yoga meditation on heart rate.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Bayes Theorem / Bayesian analysis / Bayesian statistical decision theory / Causality / Clinical trials / Discontinuity / Evaluation / Experimental research / Gaussian process / Heart rate / Humans / Interrupted Time Series Analysis / Mathematical models / Medicine and Health Sciences / Modelling / Netherlands / Nonparametric statistics / Nonparametric tests / Physical Sciences / Regression analysis / Regression models / Research and Analysis Methods / Research Design / Social Sciences / Time series