Newest papers

SCDM: Scalable Causal Discovery in Nonlinear Temporal Systems with Meta-Learning

Jingbo Wang, Kegeng Tang, Zihao Wang, Shaogang Ren · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29992 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

非线性多变时间系列的因果发现在高维系统中具有挑战性,候选人指向关系的数量随着变量数量的数量增加四分之一。现有的方法往往受到目标模型匹配,重复的条件测试或昂贵的图形搜索程序的限制。我们建议SCDM,一个可扩展的可扩展的临时因果发现的共享参数 meta-learning框架。SCDM将每个目标变量作为一个任务来处理,同时学习一个共享的临时预测器和一个跨任务的共享的因果强度矩阵。每一个基因组只更新一小组可扩展的目标任务,允许模型在整个临时系统中积累可扩展的结构证据,而无需彻底的目标优化。培训后,一个训练后的合成图谱将学习的预测

Machine translation (Meta M2M100); the English original follows.

Abstract

Causal discovery from nonlinear multivariate time series is challenging in high-dimensional systems, where the number of candidate directed relations grows quadratically with the number of variables. Existing methods are often limited by target-wise model fitting, repeated conditional testing, or expensive graph-search procedures. We propose SCDM, a shared-parameter meta-learning framework for scalable temporal causal discovery. SCDM treats each target variable as a task while learning a shared temporal predictor and a shared causal-strength matrix across tasks. Each meta-episode updates only a subset of target-variable tasks, allowing the model to accumulate structural evidence across the full system without exhaustive target-wise optimization. After training, a post-training graph readout converts the learned predictor into continuous directed causal scores for threshold-independent evaluation. We also provide a finite-error recovery principle showing that, under fully observed delayed-system assumptions and a positive risk-gap condition, thresholding SCDM scores recovers the graph when statistical, meta-optimization, and readout errors are below the population margin. Experiments on controlled synthetic, neuroimaging-inspired, high-dimensional, and real-world temporal benchmarks demonstrate competitive causal recovery and improved scalability in large temporal systems.

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Topics: Computer science, Scalability, Directed acyclic graph, Thresholding, Artificial intelligence, Machine learning

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