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Self-Discrepancy Conditional Independence Test

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Authors

Lee, Sanghack; Honavar, Vasant

Issue Date
2017
Publisher
AUAI PRESS
Citation
CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE (UAI2017)
Abstract
Tests of conditional independence (CI) of random variables play an important role in machine learning and causal inference. Of particular interest are kernel-based CI tests which allow us to test for independence among random variables with complex distribution functions. The efficacy of a CI test is measured in terms of its power and its calibratedness. We show that the Kernel CI Permutation Test (KCIPT) suffers from a loss of calibratedness as its power is increased by increasing the number of bootstraps. To address this limitation, we propose a novel CI test, called Self-Discrepancy Conditional Independence Test (SDCIT). SDCIT uses a test statistic that is a modified unbiased estimate of maximum mean discrepancy (MMD), the largest difference in the means of features of the given sample and its permuted counterpart in the kernel-induced Hilbert space. We present results of experiments that demonstrate SDCIT is, relative to the other methods: (i) competitive in terms of its power and calibratedness, outperforming other methods when the number of conditioning variables is large; (ii) more robust with respect to the choice of the kernel function; and (iii) competitive in run time.
URI
https://hdl.handle.net/10371/201563
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  • Graduate School of Data Science
Research Area Causal Decision Making, Causal Discovery, Causal Inference

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