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.

本文旨在将来自多个随机对照试验的信息传递给只有对照组数据的目标人群。我们通过源人群的治疗组的加权混合构建针对目标人群的合成治疗组，并通过最小化加权源人群的对照组与目标人群之间的条件最大均值差异来估计权重。基于筛选半参数理论，我们建立了合成治疗组估计器的渐近正态性。当均值可交换性假设被违反时，我们的方法可以作为一种新型的补充方法。我们通过对合成和真实数据集的实验来证明了我们方法的有效性。

构建无均值可交换假设的合成处理组

Constructing Synthetic Treatment Groups without the Mean Exchangeability  Assumption

The Synthetic Control method has pioneered a class of powerful data-driven
techniques to estimate the counterfactual reality of a unit from donor units.
At its core, the technique involves a linear model fitted on the
pre-intervention period that combines donor outcomes to yield the
counterfactual. However, linearly combining spatial information at each time
instance using time-agnostic weights fails to capture important inter-unit and
intra-unit temporal contexts and complex nonlinear dynamics of real data. We
instead propose an approach to use local spatiotemporal information before the
onset of the intervention as a promising way to estimate the counterfactual
sequence. To this end, we suggest a Transformer model that leverages particular
positional embeddings, a modified decoder attention mask, and a novel
pre-training task to perform spatiotemporal sequence-to-sequence modeling. Our
experiments on synthetic data demonstrate the efficacy of our method in the
typical small donor pool setting and its robustness against noise. We also
generate actionable healthcare insights at the population and patient levels by
simulating a state-wide public health policy to evaluate its effectiveness, an
in silico trial for asthma medications to support randomized controlled trials,
and a medical intervention for patients with Friedreich's ataxia to improve
clinical decision-making and promote personalized therapy.

提出了一种在干预前使用局部时空信息预估反事实序列借助于位置编码、修改的解码器注意力掩码和新的预训练任务的 Transformer 模型，可以生成有用的医疗洞见和对疾病的干预策略进行评估的结果。