用聚类有向图先验提升因果发现的准确性与效率
Cluster-Dags as Powerful Background Knowledge For Causal Discovery
- 引入聚类有向图（Cluster-DAG）作为先验知识框架，增强因果推断灵活性
- 在模拟数据上，新算法比无先验基线性能更优，尤其在高维复杂场景
- 适合需要高效处理高维数据的科研人员或工程团队使用
发现因果关系对科学至关重要。因果发现旨在从数据中恢复一个简洁描述因果关系的图结构。然而，现有方法在处理高维数据和复杂依赖时面临诸多挑战。引入系统先验知识可辅助因果发现。本文利用聚类有向图（Cluster-DAG）作为先验知识框架，用于启动因果发现过程。我们证明，相比基于分层背景知识的现有方法，Cluster-DAG具有更强的灵活性，并提出了两种改进的约束基算法：Cluster-PC（适用于完全观测情形）和Cluster-FCI（适用于部分观测情形）。在模拟数据上的实证评估表明，Cluster-PC 和 Cluster-FCI 在性能上均优于各自无先验知识的基线方法。
原文摘要 · Abstract (English)
Finding cause-effect relationships is of key importance in science. Causal discovery aims to recover a graph from data that succinctly describes these cause-effect relationships. However, current methods face several challenges, especially when dealing with high-dimensional data and complex dependencies. Incorporating prior knowledge about the system can aid causal discovery. In this work, we leverage Cluster-DAGs as a prior knowledge framework to warm-start causal discovery. We show that Cluster-DAGs offer greater flexibility than existing approaches based on tiered background knowledge and introduce two modified constraint-based algorithms, Cluster-PC and Cluster-FCI, for causal discovery in the fully and partially observed setting, respectively. Empirical evaluation on simulated data demonstrates that Cluster-PC and Cluster-FCI outperform their respective baselines without prior knowledge.
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