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.

翻译：本研究探讨了在仅有观测数据和因果图中有效因果序关系的情况下进行因果推断的问题。我们提出了一组能够恢复外生变量分量可逆变换的流模型。基于流的方法在保持因果一致性的同时提供了灵活的模型设计，且不受离散化步骤数量的影响。我们提出的设计改进实现了所有因果机制的同步学习，并将溯因与预测复杂度降低至与层数相关的线性O(n)，且独立于因果变量数量。实验表明，该方法在回答观测性、干预性和反事实性问题时，优于现有最优方法，并在多种结构因果模型中表现出稳定性能。此外，与现有基于扩散的技术相比，本方法显著减少了计算时间，使其能够适用于大规模结构因果模型。