Observational process data analytics using causal inference

Abstract

Voluminous process data are available with the paradigm shift toward smart manufacturing. However, most historical data are observational, containing noncausal correlations due to confounders and mediators. Estimating causal effects from observational data remains a bottleneck in leveraging them for active applications such as optimization and control. This work aims to introduce a causal modeling framework for analyzing observational process data and extracting quantitative causal information. We demonstrate a real-world application in steel manufacturing where causal inference is used to analyze observational production data and improve the steelmaking process.Additionally, we propose a novel formulation for identifying critical process parameters from observational data, where causal inference is combined with variance-based methods to estimate corresponding risks of interventions to the manufacturing system.The proposed methods are compared with statistical ones to illustrate that causally interpreting statistical correlation leads to problematic results, while the provided workflow generates satisfactory strategies for process improvement.

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

“…The streaming data literature offers potentially valuable works on clustering [767], pre-processing [768], outlier treatment [769,770], and event prediction [771], although we were unable to identify mutual references in the analyzed literature. Causality analysis is an example of an application based on a time series that has gained prominence in the PSE field, as evidenced by numerous studies [580,[772][773][774][775][776][777][778][779][780][781][782]. This technique uses statistical tests, such as the Granger causality test, to determine whether a given time series is useful in predicting another.…”

Causal Discovery for Topology Reconstruction in Industrial Chemical Processes

“…In our study, our main motivation is to utilize causal discovery to reconstruct the topology of chemical processes to aid in process identification and control, emphasizing the importance of accurate topological prediction. Previous works have applied causal discovery to temporal observational process data in the context of topological reconstruction. − However, these approaches often overlook very fast (essentially instantaneous) causal effects induced by different process rates and assume perfect observation of the process.…”