提出可微分排序方法，高效发现变量因果顺序。
Efficient Differentiable Discovery of Causal Order
- 用可微排序重写因果发现算法，实现连续优化
- 在真实数据上提升因果推断准确率，效果优于传统方法
- 适合需要梯度学习的基因组、气候等大规模场景
Chevalley 等人（2024）提出的 Intersort 方法基于评分机制，在干预数据下发现有向无环图（DAG）中变量的因果顺序，表现优于现有方法。然而，该方法在排列多面体上基于评分，计算成本高且不可微，难以应用于基因组、气候模型等大规模数据集，也无法融入端到端梯度学习框架。本文通过引入可微分排序与排名技术，重构 Intersort 算法。新方法支持可扩展的可微优化，使连续得分函数可作为正则项用于下游任务。实验证明，对因果顺序进行正则化显著提升因果发现性能，验证了本方法的有效性。该工作为在可微模型训练中高效引入因果顺序正则化打开新路径，解决了纯粹关联监督学习长期存在的局限。
原文摘要 · Abstract (English)
In the algorithm Intersort, Chevalley et al. (2024) proposed a score-based method to discover the causal order of variables in a Directed Acyclic Graph (DAG) model, leveraging interventional data to outperform existing methods. However, as a score-based method over the permutahedron, Intersort is computationally expensive and non-differentiable, limiting its ability to be utilised in problems involving large-scale datasets, such as those in genomics and climate models, or to be integrated into end-to-end gradient-based learning frameworks. We address this limitation by reformulating Intersort using differentiable sorting and ranking techniques. Our approach enables scalable and differentiable optimization of causal orderings, allowing the continuous score function to be incorporated as a regularizer in downstream tasks. Empirical results demonstrate that causal discovery algorithms benefit significantly from regularizing on the causal order, underscoring the effectiveness of our method. Our work opens the door to efficiently incorporating regularization for causal order into the training of differentiable models and thereby addresses a long-standing limitation of purely associational supervised learning.
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