This problem is more about causal inference than it is about censoring (and yes, causal inference is technically solving a particular subcase of censoring, but

This problem is more about causal inference than it is about censoring (and yes, causal inference is technically solving a particular subcase of censoring, but the problem setup is usually quite a bit different). They are trying to estimate the causal effect of presenting certain availability options to their customers. Their particular causal inference approach appears to be simply outcome modeling, which is an OK approach (the main problem with it is that it doesn't provide good diagnostics for detecting violation of your strong ignorability assumptions necessary for the procedure to work). They could probably improve things a bit by using a doubly robust estimator. Kaplan-Meier estimators are completely irrelevant. Those have to do with right censoring of survival distributions.