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  Testing Conditional Independence on Discrete Data using Stochastic Complexity

Marx, A., & Vreeken, J. (2019). Testing Conditional Independence on Discrete Data using Stochastic Complexity. Retrieved from http://arxiv.org/abs/1903.04829.

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arXiv:1903.04829.pdf (Preprint), 923KB
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arXiv:1903.04829.pdf
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File downloaded from arXiv at 2019-07-10 09:24 accepted at AISTATS'19, the proposed test was released in the R package SCCI
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 Creators:
Marx, Alexander1, Author           
Vreeken, Jilles1, Author           
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              

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Free keywords: Statistics, Machine Learning, stat.ML,Computer Science, Learning, cs.LG
 Abstract: Testing for conditional independence is a core aspect of constraint-based
causal discovery. Although commonly used tests are perfect in theory, they
often fail to reject independence in practice, especially when conditioning on
multiple variables.
We focus on discrete data and propose a new test based on the notion of
algorithmic independence that we instantiate using stochastic complexity.
Amongst others, we show that our proposed test, SCI, is an asymptotically
unbiased as well as $L_2$ consistent estimator for conditional mutual
information (CMI). Further, we show that SCI can be reformulated to find a
sensible threshold for CMI that works well on limited samples. Empirical
evaluation shows that SCI has a lower type II error than commonly used tests.
As a result, we obtain a higher recall when we use SCI in causal discovery
algorithms, without compromising the precision.

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Language(s): eng - English
 Dates: 2019-03-122019
 Publication Status: Published online
 Pages: 18 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 1903.04829
URI: http://arxiv.org/abs/1903.04829
BibTex Citekey: Marx_arXiv1903.04829
 Degree: -

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