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arXiv · 1910.11219

A Bayesian nonparametric test for conditional independence

Onur Teymur·Sarah Filippi
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Abstract

This article introduces a Bayesian nonparametric method for quantifying the relative evidence in a dataset in favour of the dependence or independence of two variables conditional on a third. The approach uses Polya tree priors on spaces of conditional probability densities, accounting for uncertainty in the form of the underlying distributions in a nonparametric way. The Bayesian perspective provides an inherently symmetric probability measure of conditional dependence or independence, a feature particularly advantageous in causal discovery and not employed in existing procedures of this type.

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Onur Teymur, Sarah Filippi. 2019-10-24. A Bayesian nonparametric test for conditional independence. https://doi.org/10.3934/fods.2020009

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