Domain shift creates significant challenges for sequential decision making in healthcare since the target domain may be data-scarce and confounded. In this paper, we propose a method for off-policy transfer by modeling the underlying generative process with a causal mechanism. We use informative priors from the source domain to augment counterfactual trajectories in the target in a principled manner. We demonstrate how this addresses data-scarcity in the presence of unobserved confounding. The causal parametrization of our sampling procedure guarantees that counterfactual quantities can be estimated from scarce observational target data, maintaining intuitive stability properties. Policy learning in the target domain is further regularized via the source policy through KL-divergence. Through evaluation on a simulated sepsis treatment task, our counterfactual policy transfer procedure significantly improves the performance of a learned treatment policy when assumptions of "no-unobserved confounding" are relaxed.
翻译:由于目标领域可能是数据残缺和混乱的,因此,域的改变给保健方面的连续决策带来了重大挑战。在本文中,我们提出一种方法,通过以因果机制模拟基本基因化过程来进行政策外转移。我们利用源域的信息前科,以原则方式增加目标中的反事实轨迹。我们展示了这如何在未观察到的混乱的情况下处理数据上的缺损。我们抽样程序的因果平衡保证从稀缺的观测目标数据中估计反事实数量,保持直观的稳定性能。目标领域的政策学习通过KL-调控源政策进一步规范化。通过对模拟Sepsis治疗任务的评估,我们的反事实政策转移程序在“无观测混结”假设放松时,大大改善了学习治疗政策的绩效。