Most fault-prediction, churn-prediction, and risk-prediction work defaults to binary classification: will this thing happen in the next N days, yes or no. That framing throws away information. An asset that hasn’t failed yet, a student who hasn’t dropped out yet, a patient who hasn’t had a recurrence yet — none of those are missing data points. They’re censored observations, and each one bounds the outcome even without an event attached to it. I’ve built this reframe myself for one of these problems. It turns out the same shape already exists, as documented published work, in two other domains I care … Continue reading Censored Data as Evidence: Time-to-Event Modeling Across Grid, EdTech, and Healthcare Prediction Problems