发表机构
University of Amsterdam; Radboud University Nijmegen; Saarland University（阿姆斯特丹大学; 拉德堡德大学（奈梅亨）; 萨尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何在因果发现中整合背景知识，提出在因果发现过程中利用背景知识的框架，实现可扩展的因果发现，经实验验证该方法能降低计算需求并提升学习结构质量。
AI 中文摘要
在因果发现的实际应用中，专家背景知识通常是可用的。对真实因果图的这种约束有助于因果发现，包括因果效应的可识别性和学习结构的准确性，还能减少候选因果图的空间。由于因果发现对于大量变量可能在计算上成本高昂，在因果发现过程中有效利用背景知识至关重要。然而，当前大多数方法仅在因果发现后的后处理步骤中使用背景知识来优化学习到的图。在这项工作中，我们开发了一个在因果发现过程中利用背景知识的框架，特别关注仅恢复整个图的一个子集的可扩展因果发现方法。我们为多种算法实现了我们的框架，并通过实验表明利用背景知识既可以降低计算需求，又可以提高学习结构的质量。
英文摘要
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.
