Randomized Controlled Trials (RCTs) represent a gold standard when developing policy guidelines. However, RCTs are often narrow, and lack data on broader populations of interest. Causal effects in these populations are often estimated using observational datasets, which may suffer from unobserved confounding and selection bias. Given a set of observational estimates (e.g. from multiple studies), we propose a meta-algorithm that attempts to reject observational estimates that are biased. We do so using validation effects, causal effects that can be inferred from both RCT and observational data. After rejecting estimators that do not pass this test, we generate conservative confidence intervals on the extrapolated causal effects for subgroups not observed in the RCT. Under the assumption that at least one observational estimator is asymptotically normal and consistent for both the validation and extrapolated effects, we provide guarantees on the coverage probability of the intervals output by our algorithm. To facilitate hypothesis testing in settings where causal effect transportation across datasets is necessary, we give conditions under which a doubly-robust estimator of group average treatment effects is asymptotically normal, even when flexible machine learning methods are used for estimation of nuisance parameters. We illustrate the properties of our approach on semi-synthetic and real world datasets, and show that it compares favorably to standard meta-analysis techniques.

翻译：随机对照试验（RCT）是制定政策指南的金标准。然而，RCT通常覆盖范围狭窄，缺乏更广泛目标人群的数据。这些人群中的因果效应常通过观测数据集进行估计，但此类数据可能面临未观测混杂和选择偏差问题。针对一组观测估计（例如来自多项研究的结果），我们提出了一种元算法，旨在剔除存在偏倚的观测估计。具体做法是采用验证效应——即既能从RCT数据也能从观测数据中推断出的因果效应。在剔除未通过该项检验的估计量后，我们针对RCT中未观测到的子群，生成外推因果效应的保守置信区间。假设至少存在一个观测估计量在验证效应和外推效应上均满足渐近正态性与一致性，我们提供了算法输出区间覆盖概率的理论保证。为支持需跨数据集传递因果效应的假设检验场景，我们给出了群组平均处理效应的双重稳健估计量满足渐近正态性的条件，该条件即使在使用灵活机器学习方法估计 nuisance 参数时依然成立。我们通过半合成数据集和真实数据集展示了该方法特性，并证明其优于标准元分析技术。