中文摘要
我们考察跨异质性人群（“站点”/“情境”）外推处理效应的问题。我们考虑一个理想化情景：研究者观测到若干“实验”站点中大量单元的横截面数据，其中某项干预措施已经实施；同时，对于一个新“目标”站点，已具备关于单元层面处理前结果及相关属性的基线调查数据。我们的方法将基线视为函数型数据。这一选择源于如下观察：未观测到的站点特定混杂因素不仅体现在结果的平均水平上，还体现在这些因素如何与观测到的单元特定属性相互作用。我们考虑确定最优有限维特征空间以求解该预测问题。我们采用固定总体方法，即在实验站点与目标站点的特定有限选择下评估预测器的表现。我们的方法是非参数的，形式化结果涉及最优预测基的构造，以及估计的条件平均处理效应相对于目标站点受约束最优总体预测器的收敛速率。在针对有条件现金转移（CCT）项目的应用中，我们使用来自五项多站点随机对照试验的合并数据集，量化了将实验估计适配到目标地点所能带来的潜在收益。
Abstract
We consider the problem of extrapolating treatment effects across heterogeneous populations (“sites”/“contexts”). We consider an idealized scenario in which the researcher observes cross-sectional data for a large number of units across several “experimental” sites in which an intervention has already been implemented to a new “target” site for which a baseline survey of unit-specific, pre-treatment outcomes and relevant attributes is available. Our approach treats the baseline as functional data. This choice is motivated by the observation that unobserved site-specific confounders manifest themselves not only in average levels of outcomes, but also how these interact with observed unit-specific attributes. We consider the problem of determining the optimal finite-dimensional feature space in which to solve that prediction problem. We follow a fixed-population approach which evaluates the performance of the predictor given the specific, finite selection of experimental and target sites. Our approach is nonparametric, and our formal results concern the construction of an optimal basis of predictors as well as convergence rates for the estimated conditional average treatment effect relative to the constrained-optimal population predictor for the target site. We quantify the potential gains from adapting experimental estimates to a target location in an application to conditional cash transfer (CCT) programs using a combined data set from five multi-site randomized controlled trials.