论文标题
外部有效的政策选择
Externally Valid Policy Choice
论文作者
论文摘要
我们考虑了学习外部有效或可推广的个性化治疗政策的问题：除了从中取样数据的实验（或培训）人群外，它们在其他目标人群中表现良好。我们首先表明，实验人群的福利最大化政策对实验人群和目标人群之间的结果分布（但不是特征）的变化是可靠的。然后，我们开发新方法来学习对结果和特征的转变。在这样做的过程中，我们强调了治疗在实验人群中的影响异质性如何影响政策的普遍性。我们的方法可用于实验或观察数据（治疗是内源性的）。我们的许多方法可以通过线性编程实现。
We consider the problem of learning personalized treatment policies that are externally valid or generalizable: they perform well in other target populations besides the experimental (or training) population from which data are sampled. We first show that welfare-maximizing policies for the experimental population are robust to shifts in the distribution of outcomes (but not characteristics) between the experimental and target populations. We then develop new methods for learning policies that are robust to shifts in outcomes and characteristics. In doing so, we highlight how treatment effect heterogeneity within the experimental population affects the generalizability of policies. Our methods may be used with experimental or observational data (where treatment is endogenous). Many of our methods can be implemented with linear programming.
