Causal Graph Discovery from Self and Mutually Exciting Time Series

We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series. By leveraging a recently developed stochastic monotone Variational Inequality (VI) formulation, we cast the causal dis...

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Veröffentlicht in:arXiv.org 2023-09
Hauptverfasser: Song, Wei, Xie, Yao, Josef, Christopher S, Kamaleswaran, Rishikesan
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Xie, Yao
Josef, Christopher S
Kamaleswaran, Rishikesan
description We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series. By leveraging a recently developed stochastic monotone Variational Inequality (VI) formulation, we cast the causal discovery problem as a general convex optimization. Furthermore, we develop a non-asymptotic recovery guarantee and quantifiable uncertainty by solving a linear program to establish confidence intervals for a wide range of non-linear monotone link functions. We validate our theoretical results and show the competitive performance of our method via extensive numerical experiments. Most importantly, we demonstrate the effectiveness of our approach in recovering highly interpretable causal DAGs over Sepsis Associated Derangements (SADs) while achieving comparable prediction performance to powerful ``black-box'' models such as XGBoost. Thus, the future adoption of our proposed method to conduct continuous surveillance of high-risk patients by clinicians is much more likely.
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subjects Environment models
Generalized linear models
Machine learning
Sepsis
Situational awareness
Statistical models
Surveillance systems
Time series
Upper bounds
title Causal Graph Discovery from Self and Mutually Exciting Time Series
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