arXiv Machine Learning September 7, 2026 By Glib Kechyn

How Faithful Is Attribution for Sales Forecasting? A Counterfactual Study

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The paper introduces a counterfactual interpretability layer for multi‑series WaveNet forecasters used in sales forecasting. It decomposes each forecast into contributions that exactly sum to the predicted value, avoiding allocation artifacts seen with SHAP‑style methods. The authors evaluate faithfulness via deletion/insertion tests, showing significant gaps that confirm the attributions reflect genuine model behavior, and they analyze where attribution is informative and where it is not.

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