Beyond Sufficiency: Time Series Explanation with Counterfactual Necessity

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Beyond Sufficiency: Time Series Explanation with Counterfactual Necessity

Faithful explanations of time-series classifiers should identify subsequences that are not only sufficient to preserve a black-box model's prediction, but also necessary for maintaining it. However, existing sufficiency-oriented methods can assign high importance to spurious subsequences that support the prediction without being essential to the model's decision. We introduce \textbf{TimePNS}, a necessity-aware framework for time-series explanation. Inspired by Pearl's counterfactual notion of n

Source: ArXiv AI