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Activity Number: 634 - Recent Advancements in Distance and Kernel-Based Metrics and Related Learning Methods
Type: Invited
Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #304450
Title: Distance Metrics for Measuring Joint Dependence with Application to Causal Inference
Author(s): Shubhadeep Chakraborty* and Xianyang Zhang
Companies: Texas A&M University and Texas A&M University
Keywords: Bootstrap; Directed Acyclic Graph; Distance Covariance; Interaction Dependence; U-Statistic; V-Statistic

Many statistical applications require the quantification of joint dependence among more than two random vectors. In this work, we generalize the notion of distance covariance to quantify joint dependence among d>=2 random vectors. We introduce the high order distance covariance to measure the so-called Lancaster interaction dependence. The joint distance covariance is then defined as a linear combination of pairwise distance covariances and their higher order counterparts which together completely characterize mutual independence. Empirical estimators are constructed based on certain Euclidean distances between sample elements. We study the large sample properties of the estimators and propose a bootstrap procedure to approximate their sampling distributions. The asymptotic validity of the bootstrap procedure is justified under both the null and alternative hypotheses. The new metrics are employed to perform model selection in causal inference, which is based on the joint independence testing of the residuals from the fitted structural equation models. The effectiveness of the method is illustrated via both simulated and real datasets.

Authors who are presenting talks have a * after their name.

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