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.

本研究解决了在仅有观察数据和有效因果排序的情况下的因果推断问题。提出的流模型能够恢复外生变量的成分可逆变换，并通过灵活的模型设计，实现因果一致性。研究表明，该方法在多个结构因果模型中表现优于现有技术，显著降低了计算时间，具备大规模模型的实用性。

从排序学习结构因果模型：可识别的流模型