Case-Based Explanations are an interpretability approach that explains model predictions by referencing similar past examples — "the model predicts X because this input is similar to training examples A, B, C which had outcomes Y" — leveraging the human tendency to reason by analogy.

Case-Based Explanation Methods

• k-Nearest Neighbors: Find the $k$ most similar training examples in the model's feature space.
• Influence Functions: Find training examples that most influenced the prediction (mathematically rigorous).
• Prototypes + Criticisms: Show both typical examples (prototypes) and edge cases (criticisms).
• Contrastive Examples: Show similar examples from different classes to explain decision boundaries.

Why It Matters

• Human-Natural: Humans naturally reason by analogy — case-based explanations match this cognitive style.
• No Model Assumptions: Works with any model — just need access to representations and training data.
• Domain Expert: Domain experts can validate predictions by examining whether cited cases are truly similar.

Case-Based Explanations are explaining by analogy — justifying predictions by showing similar historical cases that the model draws upon.