When the marginal causal effect comparing the same treatment pair is available from multiple trials, we wish to transport all results to make inference on the target population effect. To account for the differences between populations, statistical analysis is often performed controlling for relevant variables. However, when transportability assumptions are placed on conditional causal effects, rather than the distribution of potential outcomes, we need to carefully choose these effect measures. In particular, we present identifiability results in two cases: target population average treatment effect for a continuous outcome and causal mean ratio for a positive outcome. We characterize the semiparametric efficiency bounds of the causal effects under the respective transportability assumptions and propose estimators that are doubly robust against model misspecifications. We highlight an important discussion on the tension between the non-collapsibility of conditional effects and the variational independence induced by transportability in the case of multiple source trials.

翻译：当多个试验提供相同治疗对的边际因果效应时，我们希望整合所有结果以推断目标人群效应。为控制人群间差异，通常需针对相关变量进行统计分析。然而，若可转移性假设应用于条件因果效应而非潜在结果分布，则需审慎选择效应度量。具体而言，我们针对两种情形给出了可识别性结论：连续结局的目标人群平均处理效应与正结局的因果均值比。在相应可转移性假设下，我们刻画了因果效应的半参数效率界，并提出了对模型误设具有双重稳健性的估计量。特别地，我们强调了关于多重源试验中条件效应非可压缩性与可转移性引发的变分独立性之间张力的重要讨论。