This paper explores the application of causal discovery frameworks to infer the topology of industrial chemical processes, which is crucial for operational decision-making and system understanding. While traditional data-driven methods entail process interventions, causal discovery offers a noninvasive approach. Challenges such as temporal aggregation, subsampling, and unobserved confounders, which can lead to false predictions, are emphasized in the paper. Through simulation case studies, the performance of various causal discovery methods under different observation scenarios is evaluated. Our findings underscore the importance of simultaneously considering instantaneous and lagged causal relations, highlight the suitability of structural equation modeling for temporally aggregated processes, and caution against misinterpretation of subsampled data. Additionally, we demonstrate the utility of the Wiener separation in identifying unobserved confounders, which is essential for navigating the complexity of industrial processes.

中文翻译：

---

工业化学过程中拓扑重建的因果发现


本文探讨了因果发现框架的应用来推断工业化学过程的拓扑结构，这对于操作决策和系统理解至关重要。虽然传统的数据驱动方法需要过程干预，但因果发现提供了一种非侵入性方法。论文强调了时间聚合、二次采样和未观察到的混杂因素等可能导致错误预测的挑战。通过模拟案例研究，评估各种因果发现方法在不同观察场景下的性能。我们的研究结果强调了同时考虑瞬时和滞后因果关系的重要性，强调了结构方程模型对时间聚合过程的适用性，并警告不要误解二次采样数据。此外，我们还展示了维纳分离在识别未观察到的混杂因素方面的实用性，这对于应对工业流程的复杂性至关重要。