تقرير
Bayesian nonparametric discontinuity design
| العنوان: | Bayesian nonparametric discontinuity design |
|---|---|
| المؤلفون: | Hinne, Max, Leeftink, David, van Gerven, Marcel A. J., Ambrogioni, Luca |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Methodology, Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | 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. Comment: 15 pages, 6 figures. Parts of this work are published in 'Spectral discontinuity design: Interrupted time series with spectral mixture kernels' in the Machine Learning for Health workshop at NeurIPS 2020 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1911.06722 |
| رقم الأكسشن: | edsarx.1911.06722 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |