Conditional Mean Independence and Global Sensitivity Analysis using Nearest Neighbor Graphs

Quantifying how well a conditional mean function explains a response is central to many statistical tasks, such as model evaluation and feature screening.

A basic nonparametric measure of such dependence is the proportion of variation in the response explained by the regression function, which can also be interpreted as a multivariate Sobol' index, a fundamental notion in global sensitivity analysis.

In this paper, we propose a consistent estimator of this measure based on nearest neighbor graphs that can be computed in near-linear time.

We also derive its rate of convergence and show that a studentized version of the estimator is asymptotically standard normal under the null hypothesis of conditional mean independence.

This leads to a computationally efficient test for conditional mean independence that attains the correct asymptotic level and is universally consistent, without requiring bootstrap calibration or sample splitting.

Next, we use the proposed estimator to develop a model-free variable screening algorithm that is provably consistent.

We also discuss extensions of the framework to measuring interaction effects using higher-order Sobol' indices.

The benefits of the proposed methods are demonstrated through simulation studies and a real-data example.