The purpose of this work is to transport the information from multiple randomized controlled trials to the target population where we only have the control group data. Previous works rely critically on the mean exchangeability assumption. However, as pointed out by many current studies, the mean exchangeability assumption might be violated. Motivated by the synthetic control method, we construct a synthetic treatment group for the target population by a weighted mixture of treatment groups of source populations. We estimate the weights by minimizing the conditional maximum mean discrepancy between the weighted control groups of source populations and the target population. We establish the asymptotic normality of the synthetic treatment group estimator based on the sieve semiparametric theory. Our method can serve as a novel complementary approach when the mean exchangeability assumption is violated. Experiments are conducted on synthetic and real-world datasets to demonstrate the effectiveness of our methods.

翻译：本研究旨在将多项随机对照试验的信息迁移至仅有对照组数据的目标人群。现有方法严重依赖均值可交换性假设，但近期多项研究指出该假设可能被违反。受合成控制法启发，我们通过源人群处理组的加权混合为目标人群构建合成处理组，通过最小化源人群加权对照组与目标人群的条件最大均值差异来估计权重。基于筛分半参数理论，我们建立了合成处理组估计量的渐近正态性。当均值可交换性假设被违反时，本方法可作为新颖的互补方案。在合成数据集与真实数据集上的实验验证了方法的有效性。