将专家背景知识融入因果发现过程，提升效率与准确性
Integrating Background Knowledge for Scalable Causal Discovery
- 在发现过程中动态使用背景知识约束图结构空间
- 实验表明可降低计算开销并提升结构学习质量
- 适合大规模因果推断任务，尤其变量多时适用
实际应用中常有专家背景知识可用，这些关于真实因果图的约束能提升因果效应识别性与结构学习准确率，同时缩小候选图空间。由于高维变量下因果发现计算成本高，有效利用背景知识至关重要。然而，当前多数方法仅在因果发现后通过后处理阶段引入知识。本文提出一种在发现过程中整合背景知识的框架，重点面向仅恢复部分图结构的可扩展方法。我们为多个算法实现该框架，实证表明，利用背景知识可同时降低计算需求并提升学习结构的质量。
原文摘要 · Abstract (English)
Expert background knowledge is often available in practical applications of causal discovery. Such constraints on the true causal graph can help causal discovery in terms of identifiability of causal effects and accuracy of the learned structure, but also in reducing the space of candidate causal graphs. As causal discovery can become computationally expensive for large number of variables, it is crucial to utilize background knowledge effectively during the causal discovery process. However, most current methods only use background knowledge in a postprocessing step after causal discovery to refine the learned graph. In this work, we develop a framework for utilizing background knowledge during the causal discovery process, focusing especially on scalable causal discovery methods that recover only a subset of the whole graph. We implement our framework for multiple algorithms and empirically show that utilizing background knowledge can both reduce computational requirements and increase the quality of the learned structures.
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