Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
PLoS One. 2022 Jun 30;17(6):e0270310. doi: 10.1371/journal.pone.0270310. eCollection 2022.
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.
准实验研究设计，如回归不连续性和中断时间序列，允许在没有随机对照试验的情况下进行因果推断，但需要额外的假设。在本文中，我们提供了一个基于贝叶斯模型平均和高斯过程回归的不连续性设计框架，我们称之为“贝叶斯非参数不连续性设计”，简称 BNDD。BNDD 解决了此类设计中大多数实现的两个主要缺点：由于对所谓效果的隐含条件而导致的过度自信，以及由于依赖过于简单的回归模型而导致的模型误判。通过适当的高斯过程协方差函数，我们的方法可以检测任意阶和频谱特征的不连续性。我们在模拟中展示了 BNDD 的用法，并应用该框架来确定竞选政治职位对寿命的影响、荷兰据称的历史幻影边界对荷兰投票行为的影响，以及昆达里尼瑜伽冥想对心率的影响。
