Model-X Change-Point Detection of Conditional Distribution

TL;DR

This paper introduces MEND, a neural network-based method for detecting change points in complex, high-dimensional conditional models, improving accuracy and scalability over existing techniques.

Contribution

The paper proposes a novel, scalable Model-X change-point detection method that handles nonlinear, high-dimensional data and extends existing approaches with neural networks and distillation.

Findings

Effective in both simulation and real data

Outperforms existing methods in accuracy and scalability

Theoretically justified and computationally efficient

Abstract

The dynamic nature of many real-world systems can lead to temporal outcome model shifts, causing a deterioration in model accuracy and reliability over time. This requires change-point detection on the outcome models to guide model retraining and adjustments. However, inferring the change point of conditional models is more prone to loss of validity or power than classic detection problems for marginal distributions. This is due to both the temporal covariate shift and the complexity of the outcome model. Also, the existing method of conditional change points detection both have many limitations including linear assumption and low dimension prerequisite which sometimes is not suitable for real world application. To address these challenges, we propose a novel Model-X changE-point detectioN of conditional Distribution (MEND) method computationally enhanced with distillation function for simultaneous change-point detection and localization of the conditional outcome model. We extend and combine our model with neural network to accommodate complex nonlinear and high dimensional situation, which is proved to be valid in both simulation and real data. Theoretical validity of the proposed method is justified. Extensive simulation studies and two real-world examples demonstrate the statistical effectiveness and computational scalability of our method as well as its significant improvements over existing methods.