因果表示学习：通过非平稳性处理瞬时与滞后关系
Causal Representation Learning with Instantaneous and Lagged Relations via Nonstationarity
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中文总结 AI 辅助
针对非平稳时间序列，提出iCReN框架，利用辅助变量和对比学习识别潜在状态及其瞬时与滞后因果关系，实验验证了其准确性及预测效用。
中文摘要 AI 辅助
针对时间序列数据的因果表示学习旨在从观测中识别潜在状态及其因果关系。在此背景下，一个重要的挑战是同时建模观测区间之间的滞后因果效应以及区间内表现为瞬时关系的更快因果效应，同时考虑时间序列数据中的非平稳性。然而，能够联合处理这些因果关系与非平稳性的方法仍然有限。为填补这一空白，我们利用与转移噪声分布变化相关的观测辅助变量（如时间或条件标签），建立了在分量置换和分量可逆变换下识别潜在状态及其瞬时与滞后因果结构（直至相同置换）的充分条件。基于这些结果，我们提出了iCReN框架，该框架使用对比学习，结合离散或连续辅助变量，来学习潜在表示并估计其瞬时与滞后因果结构。实验表明，在合成数据上，该方法能准确恢复潜在状态及瞬时与滞后因果结构，并且学习到的表示对真实世界数据的下游预测具有实用价值。
英文摘要
Causal representation learning for time-series data aims to identify latent states and their causal relations from observations. In this setting, an important challenge is to model both lagged causal relations across observation intervals and faster causal effects that appear as instantaneous relations within an interval, while accounting for nonstationarity in time-series data. However, methods that jointly handle these causal relations and nonstationarity remain limited. To address this gap, we establish sufficient conditions for identifying latent states up to component permutation and component-wise invertible transformations, and their instantaneous and lagged causal structures up to the same permutation, using an observed auxiliary variable, such as time or a condition label, associated with changes in transition-noise distributions. Based on these results, we propose iCReN, a framework that uses contrastive learning with discrete or continuous auxiliary variables to learn latent representations and estimate their instantaneous and lagged causal structures. Experiments demonstrate accurate recovery of latent states and both instantaneous and lagged causal structures on synthetic data and the utility of the learned representations for downstream forecasting on real-world data.
发表机构
- The University of Osaka（大阪大学）
- RIKEN（理化学研究所）
- Shiga University（滋贺大学）
机构由 AI 辅助整理，请以论文原文为准。
