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Title: Self-Discrepancy Conditional Independence Test
Tests of conditional independence (CI) of ran- dom variables play an important role in ma- chine learning and causal inference. Of partic- ular interest are kernel-based CI tests which allow us to test for independence among ran- dom variables with complex distribution func- tions. 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 limita- tion, 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 experi- ments 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 condition- ing variables is large; (ii) more robust with re- spect to the choice of the kernel function; and (iii) competitive in run time.  more » « less
Award ID(s):
1636795
PAR ID:
10050475
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Uncertainty in artificial intelligence
Volume:
33
ISSN:
1525-3384
Page Range / eLocation ID:
http://auai.org/uai2017/proceedings/papers/16.pdf
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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