A Kernel-Based Nonparametric Test for Conditional Independence of Functional Data

TL;DR

This paper introduces a novel kernel-based nonparametric test for assessing conditional independence between random functions, extending existing multivariate methods to the functional data setting, with theoretical guarantees and practical applications.

Contribution

We develop the first conditional independence test for functional data using the conjoined conditional covariance operator, with rigorous asymptotic analysis and real-world applications.

Findings

The test accurately detects conditional independence in functional data.

The asymptotic distribution of the test statistic is derived and validated.

Applications demonstrate the method's effectiveness on activity, biometrics, and macroeconomic datasets.

Abstract

Conditional independence is a fundamental concept in many areas of statistical research, including, for example, sufficient dimension reduction, causal inference, and statistical graphical models. In many modern applications, data arise in the form of random functions, making it important to determine whether two random functions are conditionally independent given a third. However, to the best of our knowledge, existing conditional independence tests in the literature apply only to multivariate data, and extensions to the functional setting are not available. To fill this gap, we develop a kernel-based test for conditional independence of random functions based on the conjoined conditional covariance operator (CCCO). We rigorously derive the asymptotic distribution of the CCCO estimator using a recently established sharpened convergence rate for the regression operator (Choi et al., 2026). Based on this result, we construct a test statistic using the spectral decomposition of the operator appearing in the asymptotic distribution. The proposed method is illustrated through applications to an activity and biometrics dataset and a macroeconomic dataset.