Bayesian nonparametric discontinuity design
This provides a more robust method for causal inference in social sciences and health studies, though it is incremental as it builds on existing discontinuity designs.
The paper tackles the problem of overconfidence and model misspecification in quasi-experimental designs like regression discontinuity by introducing a Bayesian nonparametric framework using Gaussian processes, which can detect various types of discontinuities and is applied to simulations and real-world cases such as political longevity and meditation effects.
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.