What Moved the Alarm Score? Causal Attribution with Exact Computation and Scarce-Incident Estimation
Read the original on arXiv Machine Learning →The paper introduces a method for attributing changes in alarm scores in industrial and scientific systems by modeling the problem as a Shapley game over hybrid structural causal models. It distinguishes between changes in structural functions and exogenous laws, providing a detailed, function/law-resolved explanation of how each type of change propagates to the alarm score. For ordered linear SCMs and quadratic scores, the authors present a polynomial‑time exact computation of the full Shapley vector and, for scarce incident data, a cross‑fitted estimation approach with error bounds and top‑k recovery guarantees, validated through experiments on controlled and real shifts.
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