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Continuous Treatment Recommendation with Deep Survival Dose Response Function. (arXiv:2108.10453v4 [stat.ML] UPDATED)
stat.ML updates on arXiv.org arxiv.org
We propose a general formulation for continuous treatment recommendation
problems in settings with clinical survival data, which we call the Deep
Survival Dose Response Function (DeepSDRF). That is, we consider the problem of
learning the conditional average dose response (CADR) function solely from
historical data in which observed factors (confounders) affect both observed
treatment and time-to-event outcomes. The estimated treatment effect from
DeepSDRF enables us to develop recommender algorithms with the correction for
selection bias. We compared two recommender approaches …
arxiv continuous function ml recommendation survival treatment