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 based on random search and reinforcement learning and found similar performance in terms of patient outcome. We tested the DeepSDRF and the corresponding recommender on extensive simulation studies and the eICU Research Institute (eRI) database. To the best of our knowledge, this is the first time that causal models are used to address the continuous treatment effect with observational data in a medical context.

翻译：我们提出了一种在临床生存数据场景下处理连续治疗推荐问题的通用框架，称为深度生存剂量反应函数（DeepSDRF）。具体而言，我们考虑仅从历史数据中学习条件平均剂量反应（CADR）函数的问题，其中观测因素（混杂变量）同时影响观测到的治疗变量和事件时间结局。DeepSDRF估计的治疗效应使我们能够开发具有选择偏差校正功能的推荐算法。我们比较了基于随机搜索和强化学习的两种推荐方法，发现两者在患者结局方面表现相近。我们在大量仿真研究及eICU研究所（eRI）数据库上测试了DeepSDRF及其对应的推荐算法。据我们所知，这是首次在医疗场景中利用因果模型处理基于观测数据的连续治疗效应问题。