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auton-survival: An Open-Source Package for Regression, Counterfactual Estimation, Evaluation

Date:

Authors: Chirag Nagpal, Willa Potosnak

Topics: Operations Research, Predictive Maintenance

Organizations: Carnegie Melon

Real-world decision-making often requires reasoning about when an event will occur. The overarching goal of such reasoning is to help aid decision-making for optimal triage and subsequent intervention. Such problems involving estimation of Times-to-an-Event frequently arise across multiple application areas, including, predictive maintenance. Reliability engineering and systems safety research involves the use of remaining useful life prediction models to help extend the longevity of machinery and equipment by proactive part and component replacement.

Discretizing time-to-event outcomes to predict if an event will occur is a common approach in standard machine learning. However, this neglects temporal context, which could result in models that misestimate and lead to poorer generalization.

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