基于变量顺序的因果模型，用流模型实现可逆变换与高效推断。
Learning Structural Causal Models from Ordering: Identifiable Flow Models
- 利用流模型重构外生变量的可逆变换，保持因果一致性。
- 在多种因果模型上表现优于现有方法，且计算复杂度降至O(n)。
- 适合需要高效因果推断的大规模结构化模型研究者使用。
本研究解决仅有观测数据和有效因果顺序时的因果推断问题。我们提出一组流模型，能够恢复外生变量的分量可逆变换。该方法在任意离散化步数下均保持因果一致性，支持灵活建模。通过设计改进，实现了所有因果机制的同时学习，并将反事实推断与预测复杂度降低至相对于层数的线性复杂度O(n)，独立于因果变量数量。实证结果表明，该方法在回答观测、干预和反事实问题时均优于现有最先进方法，在各类结构因果模型中表现稳定。此外，相比现有扩散方法，计算时间显著减少，适用于大规模结构因果模型。
原文摘要 · Abstract (English)
In this study, we address causal inference when only observational data and a valid causal ordering from the causal graph are available. We introduce a set of flow models that can recover component-wise, invertible transformation of exogenous variables. Our flow-based methods offer flexible model design while maintaining causal consistency regardless of the number of discretization steps. We propose design improvements that enable simultaneous learning of all causal mechanisms and reduce abduction and prediction complexity to linear O(n) relative to the number of layers, independent of the number of causal variables. Empirically, we demonstrate that our method outperforms previous state-of-the-art approaches and delivers consistent performance across a wide range of structural causal models in answering observational, interventional, and counterfactual questions. Additionally, our method achieves a significant reduction in computational time compared to existing diffusion-based techniques, making it practical for large structural causal models.
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