Non-Parametric Inference of Relational Dependence
- URL: http://arxiv.org/abs/2207.00163v1
- Date: Thu, 30 Jun 2022 03:42:20 GMT
- Title: Non-Parametric Inference of Relational Dependence
- Authors: Ragib Ahsan, Zahra Fatemi, David Arbour, Elena Zheleva
- Abstract summary: This work examines the problem of estimating independence in data drawn from relational systems.
We propose a consistent, non-parametric, scalable kernel test to operationalize the relational independence test for non-i.i.d. observational data.
- Score: 17.76905154531867
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Independence testing plays a central role in statistical and causal inference
from observational data. Standard independence tests assume that the data
samples are independent and identically distributed (i.i.d.) but that
assumption is violated in many real-world datasets and applications centered on
relational systems. This work examines the problem of estimating independence
in data drawn from relational systems by defining sufficient representations
for the sets of observations influencing individual instances. Specifically, we
define marginal and conditional independence tests for relational data by
considering the kernel mean embedding as a flexible aggregation function for
relational variables. We propose a consistent, non-parametric, scalable kernel
test to operationalize the relational independence test for non-i.i.d.
observational data under a set of structural assumptions. We empirically
evaluate our proposed method on a variety of synthetic and semi-synthetic
networks and demonstrate its effectiveness compared to state-of-the-art
kernel-based independence tests.
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