Year: 2021
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Wei, Song, Xie, Yao, Josef, Christopher S., Kamaleswaran, Rishikesan
Institutions: Georgia Institute of Technology, Emory University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: ATD: Scanning Dynamic Spatial-Temporal Discrete Events for Threat Detection, Bridging Statistical Hypothesis Tests and Deep Learning for Reliability and Computational Efficiency, CAREER: Quick Detection for Streaming Data Over Dynamic Networks, G3387330441, G4324459455, Data-Driven Optimal Police Patrol Zone Districting and Staffing, Sequential Detection and Prediction for Solar Situation Awareness in Power Networks, AI Institute for Advances in Optimization, G6127310570, G6671297155, G7238632903, G7911581280, G975068223
OpenAlex ID: W4312224485