Bayesian nonparametric discontinuity design

TL;DR

This paper introduces Bayesian nonparametric discontinuity design (BNDD), a flexible framework using Gaussian processes for causal inference in quasi-experimental studies, addressing model misspecification and overconfidence issues.

Contribution

BNDD provides a Bayesian approach with Gaussian processes to detect discontinuities of any order, improving robustness over traditional methods.

Findings

Successfully applied BNDD to real-world case studies.

Demonstrated improved detection of discontinuities over traditional models.

Showed BNDD's ability to handle spectral features and complex discontinuities.

Abstract

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 comparison 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.