论文标题
学会有效地搜索因果关系近乎最佳的治疗
Learning to search efficiently for causally near-optimal treatments
论文作者
论文摘要
寻找有效的医疗通常需要通过反复试验进行搜索。通过最大程度地减少不必要的试验的数量，可以降低成本和患者的痛苦，从而更加有效。我们将这个问题形式化为学习一种政策，以使用因果推理框架进行最少的试验中找到近乎最佳的治疗方法。我们提供了一种基于模型的动态编程算法，该算法从观察数据中学习，同时对无法测量的混杂性进行了强大的态度。为了降低时间的复杂性，我们建议一种贪婪的算法，该算法界定了近距离限制。这些方法是根据合成和现实世界的医疗保健数据进行评估的，并将其与无模型的增强学习进行了比较。我们发现，我们的方法与无模型基线相比，同时提供搜索时间和治疗功效之间的更透明的权衡。
Finding an effective medical treatment often requires a search by trial and error. Making this search more efficient by minimizing the number of unnecessary trials could lower both costs and patient suffering. We formalize this problem as learning a policy for finding a near-optimal treatment in a minimum number of trials using a causal inference framework. We give a model-based dynamic programming algorithm which learns from observational data while being robust to unmeasured confounding. To reduce time complexity, we suggest a greedy algorithm which bounds the near-optimality constraint. The methods are evaluated on synthetic and real-world healthcare data and compared to model-free reinforcement learning. We find that our methods compare favorably to the model-free baseline while offering a more transparent trade-off between search time and treatment efficacy.
