PeCE: A Visual Analytics Paradigm for Enhancing Counterfactual Explanations

Counterfactual explanations offer an intuitive way to understand the prediction behavior of machine learning models. However, they still face major challenges in practice. Explanation quality is often unclear due to feature redundancy and the lack of systematic evaluation. The resulting information is fragmented and hard to integrate into a coherent decision logic. To address these issues, this paper proposes a new analysis paradigm. It reframes counterfactual explanation as an auditable and iterative investigative reasoning workflow. The paradigm consists of three tightly coupled steps. First, it focuses on causal features to reduce the initial complexity of the explanation space. This is achieved through the Minimal Feature Boundary (MFB) and interactive constraint modeling. Second, it introduces multi-dimensional quality audit views. These views incorporate metrics such as the Counterfactual Equilibrated Quality Score (CEQS). They support evidence-based filtering and comparison of counterfactual candidates. Third, it establishes a visualization-driven hypothesis testing loop. This loop links coordinated views to distribution comparison and causal exploration. It allows users to dynamically validate and iteratively refine their understanding of model's behavior. The paradigm is instantiated in the visual analytics system PeCE. Its effectiveness is demonstrated through case studies and expert interviews. The results show that the proposed paradigm improves transparency and traceability in the counterfactual explanation process. As a result, counterfactual explanations become more credible and practical as diagnostic tools.