A Bayesian nonparametric test for conditional independence

O Teymur, S Filippi - arXiv preprint arXiv:1910.11219, 2019 - arxiv.org
arXiv preprint arXiv:1910.11219, 2019arxiv.org
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 …
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.
arxiv.org
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