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Title: Towards Conditional Independence Test for Relational Data
Conditional independence (CI) tests play a central role in statistical inference, machine learning, and causal discovery. Most existing CI tests assume that the samples are indepen- dently and identically distributed (i.i.d.). How- ever, this assumption often does not hold in the case of relational data. We define Relational Conditional Independence (RCI), a generaliza- tion of CI to the relational setting. We show how, under a set of structural assumptions, we can test for RCI by reducing the task of test- ing for RCI on non-i.i.d. data to the problem of testing for CI on several data sets each of which consists of i.i.d. samples. We develop Kernel Relational CI test (KRCIT), a nonpara- metric test as a practical approach to testing for RCI by relaxing the structural assumptions used in our analysis of RCI. We describe re- sults of experiments with synthetic relational data that show the benefits of KRCIT relative to traditional CI tests that don’t account for the non-i.i.d. nature of relational data.  more » « less
Award ID(s):
1636795
NSF-PAR ID:
10050478
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/49.pdf
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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