用标签精准判断因果关系方向，提升发现准确率。
Tagged for Direction: Pinning Down Causal Edge Directions with Precision
- 为变量赋予多个标签，通过标签间关系推断因果方向
- 实验表明该方法显著提升因果发现性能，符合常识认知
- 适合需要高精度因果推理的科研与医疗数据分析场景
并非所有变量间的因果关系都相同，这一差异可被用于因果发现任务。近期研究发现，特定类型配对的变量会对其余同类型变量对的因果方向产生偏好。尽管有效，但在实际中为变量分配特定类型存在困难。本文提出基于标签的因果发现方法：在因果图中为每个变量分配多个标签；先使用现有方法确定部分边的方向，再据此推断标签间的因果关系；最后利用这些标签关系来定向未定向的边。相比仅依赖单一类型的传统方法，该方法摆脱了类型一致性假设的限制，更具鲁棒性与灵活性。实验结果表明，该方法显著提升了因果发现效果，且标签间关系符合常见知识。
原文摘要 · Abstract (English)
Not every causal relation between variables is equal, and this can be leveraged for the task of causal discovery. Recent research shows that pairs of variables with particular type assignments induce a preference on the causal direction of other pairs of variables with the same type. Although useful, this assignment of a specific type to a variable can be tricky in practice. We propose a tag-based causal discovery approach where multiple tags are assigned to each variable in a causal graph. Existing causal discovery approaches are first applied to direct some edges, which are then used to determine edge relations between tags. Then, these edge relations are used to direct the undirected edges. Doing so improves upon purely type-based relations, where the assumption of type consistency lacks robustness and flexibility due to being restricted to single types for each variable. Our experimental evaluations show that this boosts causal discovery and that these high-level tag relations fit common knowledge.
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