Causal Knowledge in Data Fusion: Systematic Evaluation on Quality Prediction and Root Cause Analysis

Data fusion deals with combining information from multiple sensors to support decision making. In such settings, machine learning methods, that principally only take correlation into account, have been applied widely due to their strong predictive and computational capabilities. In this paper, we investigate potential benefits of introducing causal knowledge in machine learning-based data fusion to address two common downstream tasks, namely, quality prediction and root cause analysis (RCA). To resemble the complex relationships typically associated with sensor data, we create simulation data with explicit modeling of latent confounding. The results of this study indicate that taking into account true causal knowledge significantly improves the performance of RCA, and leads to prediction models that are more robust to severe distribution shifts in the presence of latent confounding. Furthermore, if causal knowledge needs to be inferred from observational data using existing causal discovery methods, we propose a selection criterion to choose the best causal structure. We show that given a sufficient amount of data, the selected causal structure can be used as reliable input to solve the downstream tasks.