多数据集因果发现新方法，能处理变量不完全重叠的问题
I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables
- 基于带未观测变量的因果加性模型，融合多个数据集结果
- 通过枚举结构一致的图，恢复被隐藏的因果关系
- 适用于变量缺失或不完全重合的科学数据分析
从观测数据中进行因果发现是科学领域的基础工具。尽管现有方法通常针对单一数据集设计，实际中常需处理变量集合不完全相同的多个数据集。简单做法是分别估计各数据集的因果图并取交集，但受限于各数据集中未观测变量可能作为混杂因子，且部分变量对可能在任一数据集中均未被观测。为此，本文利用包含未观测变量的因果加性模型（CAM-UV），其可提供与未观测变量相关的因果信息。我们证明真实因果图与每个数据集上CAM-UV所揭示的信息具有结构一致性。据此，提出I-CAM-UV方法，通过枚举所有一致的因果图来整合结果，并设计了高效的组合搜索算法。实验表明，该方法在多个基准上优于现有方法。
原文摘要 · Abstract (English)
Causal discovery from observational data is a fundamental tool in various fields of science. While existing approaches are typically designed for a single dataset, we often need to handle multiple datasets with non-identical variable sets in practice. One straightforward approach is to estimate a causal graph from each dataset and construct a single causal graph by overlapping. However, this approach identifies limited causal relationships because unobserved variables in each dataset can be confounders, and some variable pairs may be unobserved in any dataset. To address this issue, we leverage Causal Additive Models with Unobserved Variables (CAM-UV) that provide causal graphs having information related to unobserved variables. We show that the ground truth causal graph has structural consistency with the information of CAM-UV on each dataset. As a result, we propose an approach named I-CAM-UV to integrate CAM-UV results by enumerating all consistent causal graphs. We also provide an efficient combinatorial search algorithm and demonstrate the usefulness of I-CAM-UV against existing methods.
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