Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis

TL;DR

This paper introduces CD-RCA, a causal-discovery-based method for root-cause analysis of prediction errors in machine learning, which does not require predefined causal graphs and effectively identifies variables contributing to outliers.

Contribution

The paper proposes a novel causal-discovery approach for root-cause analysis that estimates causal relationships directly from data, improving accuracy over heuristic methods without needing predefined causal structures.

Findings

CD-RCA outperforms existing heuristic attribution methods.

It accurately identifies variables contributing to prediction error outliers.

The method effectively estimates causal relationships from synthetic data.

Abstract

Recent rapid advancements of machine learning have greatly enhanced the accuracy of prediction models, but most models remain "black boxes", making prediction error diagnosis challenging, especially with outliers. This lack of transparency hinders trust and reliability in industrial applications. Heuristic attribution methods, while helpful, often fail to capture true causal relationships, leading to inaccurate error attributions. Various root-cause analysis methods have been developed using Shapley values, yet they typically require predefined causal graphs, limiting their applicability for prediction errors in machine learning models. To address these limitations, we introduce the Causal-Discovery-based Root-Cause Analysis (CD-RCA) method that estimates causal relationships between the prediction error and the explanatory variables, without needing a pre-defined causal graph. By simulating synthetic error data, CD-RCA can identify variable contributions to outliers in prediction errors by Shapley values. Extensive experiments show CD-RCA outperforms current heuristic attribution methods.