We present a general framework to target customers using optimal targeting policies, and we document the profit differences from alternative estimates of the optimal targeting policies. Two foundations of the framework are conditional average treatment effects (CATEs) and off-policy evaluation using data with randomized targeting. This policy evaluation approach allows us to evaluate an arbitrary number of different targeting policies using only one randomized data set and thus provides large cost advantages over conducting a corresponding number of field experiments. We use different CATE estimation methods to construct and compare alternative targeting policies. Our particular focus is on the distinction between indirect and direct methods. The indirect methods predict the CATEs using a conditional expectation function estimated on outcome levels, whereas the direct methods specifically predict the treatment effects of targeting. We introduce a new direct estimation method called treatment effect projection (TEP). The TEP is a non-parametric CATE estimator that we regularize using a transformed outcome loss which, in expectation, is identical to a loss that we could construct if the individual treatment effects were observed. The empirical application is to a catalog mailing with a high-dimensional set of customer features. We document the profits of the estimated policies using data from two campaigns conducted one year apart, which allows us to assess the transportability of the predictions to a campaign implemented one year after collecting the training data. All estimates of the optimal targeting policies yield larger profits than uniform policies that target none or all customers. Further, there are significant profit differences across the methods, with the direct estimation methods yielding substantially larger economic value than the indirect methods.

我们提出了一个使用最佳定位政策来定位客户的总体框架，并记录了与最佳定位政策的替代估计的利润差异。该框架的两个基础是条件平均治疗效果 (CATE) 和使用随机目标数据进行的非政策评估。这种政策评估方法使我们能够仅使用一个随机数据集来评估任意数量的不同目标政策，因此与进行相应数量的现场实验相比，具有巨大的成本优势。我们使用不同的 CATE 估计方法来构建和比较替代的目标政策。我们特别关注间接方法和直接方法之间的区别。间接方法使用根据结果水平估计的条件期望函数来预测 CATE，而直接方法则专门预测靶向的治疗效果。我们引入了一种新的直接估计方法，称为治疗效果投影（TEP）。 TEP 是一个非参数 CATE 估计量，我们使用转换后的结果损失对其进行正则化，预计该损失与观察个体治疗效果时我们可以构建的损失相同。实证应用是具有高维客户特征集的目录邮寄。我们使用相隔一年进行的两次活动的数据来记录估计政策的利润，这使我们能够评估预测到收集训练数据一年后实施的活动的可移植性。与不针对任何客户或针对所有客户的统一政策相比，对最佳目标政策的所有估计都会产生更大的利润。此外，各种方法之间存在显着的利润差异，直接估计方法产生的经济价值比间接方法大得多。