What Moved the Alarm Score? Causal Attribution with Exact Computation and Scarce-Incident Estimation

Key points

• Alarms in industrial systems and scientific instruments often demand immediate diagnosis despite scarce incident data.1
• To guide intervention, an explanation must trace how changes in the data-generating process propagate through the causal structure to alter the alarm score, rather than merely flagging shifts that may be downstream effects.1
• Yet exact attribution across interacting causal components appears combinatorial, while existing methods often either assume an unchanged causal model or require enough incident data to estimate the changed system reliably.1
• To estimate this target from scarce incident data, we adapt a well-observed reference SCM to the incident regime using cross-fitted estimates of mechanism changes and residual moments, and derive attribution-error bounds and top-k recovery guarantees.1

Sources (1)

• [1]What Moved the Alarm Score? Causal Attribution with Exact Computation and Scarce-Incident Estimation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 02:50 AM

  We address this setting by formulating the alarm-score change as a Shapley game over hybrid structural causal model (SCM) states.

  Alarms in industrial systems and scientific instruments often demand immediate diagnosis despite scarce incident data.

Extractive summary: sentences quoted from the sources.