Contrast feature dependency pattern mining for controlled experiments with application to driving behavior

Abstract

A controlled experiment is an empirical interventional study method to evaluate the causal impact of an intervention, by identifying the dynamic feature dependency patterns in the contrast multivariate time series (CMTS) collected from the control and experimental groups. Manually labeling or interpreting the effects caused by the intervention from the CMTS data has become an infeasible task even for domain experts. Thus, it is imperative to develop an integrated technique, preferably in an unsupervised manner, that can simultaneously identify and characterize feature dynamic dependencies and their contrast patterns in CMTS, which we call the contrast dynamic feature dependency (CDFD) patterns. In this paper, we propose a generative model with partial correlation-based feature dependency regularization to help analysts understand the CMTS data by jointly 1) characterizing a set of comparable multivariate Gaussian distributions from CMTS, and 2) determining whether the intervention causes the changes between two comparable distributions. Extensive experiments demonstrate the effectiveness and scalability of the proposed method. The proposed method applied to a driving behavior application demonstrates its utility and interpretability.