A Bayesian nonparametric test for conditional independence

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.

References in corpus (5)

• Kernel-based Conditional Independence Test and Application in Causal Discovery
• Causal Discovery with Continuous Additive Noise Models
• A Scalable Conditional Independence Test for Nonlinear, Non-Gaussian Data
• Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information
• Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes

Cited by in corpus (1)

• A Bayesian Nonparametric Conditional Two-sample Test with an Application to Local Causal Discovery