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arXiv Machine Learning By Felix Saretzky

PriMa-Causa: Causal Prescriptive Maintenance

Read the original on arXiv Machine Learning →

PriMa-Causa is a new framework for causal prescriptive maintenance that ranks maintenance actions by their estimated interventional effect. It combines a causal reasoning layer, a parametric simulator that generates interventional ground truth, and a graph-conditioned tabular causal foundation model trained once on synthetic production‑line data. The approach achieves lower prescription error than associational baselines, especially under weak overlap and sparse context, and shows further improvement when a partially disclosed causal graph is used.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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