Detecting Confounders in Multivariate Time Series using Strength of Causation

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...

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Bibliographic Details
Published in2023 31st European Signal Processing Conference (EUSIPCO) pp. 1400 - 1404
Main Authors Liu, Yuhao, Cui, Chen, Waxman, Daniel, Butler, Kurt, Djuric, Petar M.
Format Conference Proceeding
LanguageEnglish
Published EURASIP 04.09.2023
Subjects
Europe
Gaussian processes
Signal processing
Time series analysis
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Summary: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.
ISSN:2076-1465
DOI:10.23919/EUSIPCO58844.2023.10289850