论文标题
在临床环境中进行反事实引导的非政策转移
Counterfactually Guided Off-policy Transfer in Clinical Settings
论文作者
论文摘要
在为新患者人群使用训练有素的模型时遇到的域转移会为医疗保健中的顺序决策带来重大挑战，因为目标领域可能既是数据筛选又混淆。在本文中，我们提出了一种通过因果机制对基本生成过程进行建模的基础转移方法。我们以原则上的方式使用来自源域的信息先验来增强目标中的反事实轨迹。我们演示了在未观察到的混杂存在下如何解决数据划分。我们的抽样程序的因果参数化确保了反事实数量可以从稀缺的观察目标数据估算，从而维持直观的稳定性。通过KL-Divermence通过源政策进一步正规化目标领域的政策学习。通过对模拟败血症治疗任务的评估，当假设“未固定混淆”的假设放松时，我们的反事实政策转移程序可显着提高学习治疗政策的性能。
Domain shift, encountered when using a trained model for a new patient population, creates significant challenges for sequential decision making in healthcare since the target domain may be both 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.
