Causal Structural Learning from Time Series: A Convex Optimization Approach

• URL: http://arxiv.org/abs/2301.11336v2
• Date: Sat, 15 Apr 2023 20:23:17 GMT
• Title: Causal Structural Learning from Time Series: A Convex Optimization Approach
• Authors: Song Wei, Yao Xie
• Abstract summary: Structural learning aims to learn directed acyclic graphs (DAGs) from observational data. Recent DAG learning remains a highly non-adaptive structural learning problem. We propose a data approach for causal learning using a recently developed monotone variational (VI) formulation.
• Score: 12.4517307615083
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Structural learning, which aims to learn directed acyclic graphs (DAGs) from observational data, is foundational to causal reasoning and scientific discovery. Recent advancements formulate structural learning into a continuous optimization problem; however, DAG learning remains a highly non-convex problem, and there has not been much work on leveraging well-developed convex optimization techniques for causal structural learning. We fill this gap by proposing a data-adaptive linear approach for causal structural learning from time series data, which can be conveniently cast into a convex optimization problem using a recently developed monotone operator variational inequality (VI) formulation. Furthermore, we establish non-asymptotic recovery guarantee of the VI-based approach and show the superior performance of our proposed method on structure recovery over existing methods via extensive numerical experiments.

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