Detecting Confounders in Multivariate Time Series using Strength of Causation
Yuhao Liu, Chen Cui, Daniel Waxman, Kurt Butler, Petar M. Djurić · 2023
One of the most important problems in science is understanding causation. This is particularly challenging when one has access to observational data only and is further compounded in the presence of latent confounders. In this paper, we propose a method for detecting confounders in multivariate time series using a recently introduced concept referred to as differential causal effect (DCE). The solution is based on feature-based Gaussian processes that are used for estimating both, the DCE of the observed time series and the latent confounders. We demonstrate the performance of the proposed method with several examples. They show that the proposed approach can detect confounders and can accurately estimate causal strengths.