论文标题
通过目标样本摘要信息进行因果概括的熵平衡
Entropy Balancing for Causal Generalization with Target Sample Summary Information
论文作者
论文摘要
在本文中，我们专注于估计目标人群的平均治疗效果（ATE），当来自源总数的个别数据和摘要级别的数据（例如，某些协变量的第一或第二矩）提供了目标人群的平均治疗效果（ATE）。在存在异质治疗效果的情况下，当这两个种群中的治疗效果改性剂不同时，目标群体的ATE与源人群的ATE可能不同，这一现象也称为协方差转移。已经开发了许多方法来调整协变量偏移，但是大多数需要来自代表性目标样本的单个协变量。我们基于来自目标样本的摘要级信息开发一种加权方法，以调整效应修饰符的可能协方差转移。特别是，通过目标样本的摘要级别信息来校准源样本中处理过和对照组的权重。我们的方法还寻求源样本中处理组和对照组之间的其他协变量平衡。我们研究了在广泛的条件下，目标人群对相应加权估计量的渐近行为。理论含义在模拟研究和实际数据应用中得到了证实。
In this paper, we focus on estimating the average treatment effect (ATE) of a target population when individual-level data from a source population and summary-level data (e.g., first or second moments of certain covariates) from the target population are available. In the presence of heterogeneous treatment effect, the ATE of the target population can be different from that of the source population when distributions of treatment effect modifiers are dissimilar in these two populations, a phenomenon also known as covariate shift. Many methods have been developed to adjust for covariate shift, but most require individual covariates from a representative target sample. We develop a weighting approach based on summary-level information from the target sample to adjust for possible covariate shift in effect modifiers. In particular, weights of the treated and control groups within a source sample are calibrated by the summary-level information of the target sample. Our approach also seeks additional covariate balance between the treated and control groups in the source sample. We study the asymptotic behavior of the corresponding weighted estimator for the target population ATE under a wide range of conditions. The theoretical implications are confirmed in simulation studies and a real data application.
