Dynamical causality under invisible confounders
不可观测混杂因素下的动力系统因果关系AI 解读 ↓
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不可观测混杂因素下的动力系统因果关系
- 研究问题
- 存在多个不可观测混杂因素时，如何从有限观测变量的时间序列中辨识动力系统因果关系，并重建隐藏混杂及分离相互纠缠的变量？
- 方法与思路
- 作者提出 CIC 方法，在延迟嵌入空间建立正交分解，将观测变量分为公共与私有子空间，据此量化因果关系并重建不可观测混杂。
- 材料陈述的贡献
- 摘要称，该理论支持多隐藏混杂下的因果检测并处理不可分离问题；真实数据实验展示了在未观测混杂下重建生物网络的效果。
- 阅读关注点
- 应重点核对正交分解定理与延迟嵌入的条件、动力因果关系的定义，以及仅有两个观测变量时的辨识依据和实验验证标准。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未经核验；摘要未列出定理完整条件，不能将其广泛适用性陈述当作已验证结论。
原文摘要
Causality inference is prone to spurious causal interactions, due to the substantial confounders in a complex system. While many existing methods based on the statistical methods or dynamical methods attempt to address misidentification challenges, there remains a notable lack of effective methods to infer causality, in particular in the presence of invisible/unobservable confounders. As a result, accurately inferring causation with invisible confounders remains a largely unexplored and outstanding issue in data science and AI fields. In this work, we propose a method to overcome such challenges to infer dynamical causality under invisible confounders (CIC method) and further reconstruct the invisible confounders from time-series data by developing an orthogonal decomposition theorem in a delay embedding space. The core of our CIC method lies in its ability to decompose the observed variables not in their original space but in their delay embedding space into the common and private subspaces respectively, thereby quantifying causality between those variables both theoretically and computationally. This theoretical foundation ensures the causal detection for any high-dimensional system even with only two observed variables under many invisible confounders, which is actually a long-standing problem in the field. In addition to the invisible confounder problem, such a decomposition actually makes the intertwined variables separable in the embedding space, thus also solving the non-separability problem of causal inference. Extensive validation of the CIC method is carried out using various real datasets, and the experimental results demonstrates its effectiveness to reconstruct real biological networks even with unobserved confounders.