论文标题
在连续治疗环境中进行非政策评估的深度跳跃学习
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings
论文作者
论文摘要
我们考虑在连续治疗环境中（例如个性化剂量调查）中考虑非政策评估（OPE）。在OPE中，一个人旨在使用不同决策规则产生的历史数据在新的治疗决策规则下估算平均结果。 OPE上的大多数现有作品都集中在离散治疗环境上。为了处理连续的治疗，我们使用深跳学习开发了一种新颖的OPE估计方法。我们方法的关键要素在于利用深度学习和多规模变化点检测来适应使用深层离散化的治疗空间。这使我们能够在离散治疗中应用现有的OPE方法来处理连续治疗。理论结果，模拟和对华法林给药的实际应用进一步证明了我们的方法。
We consider off-policy evaluation (OPE) in continuous treatment settings, such as personalized dose-finding. In OPE, one aims to estimate the mean outcome under a new treatment decision rule using historical data generated by a different decision rule. Most existing works on OPE focus on discrete treatment settings. To handle continuous treatments, we develop a novel estimation method for OPE using deep jump learning. The key ingredient of our method lies in adaptively discretizing the treatment space using deep discretization, by leveraging deep learning and multi-scale change point detection. This allows us to apply existing OPE methods in discrete treatments to handle continuous treatments. Our method is further justified by theoretical results, simulations, and a real application to Warfarin Dosing.
