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:在制定政策指南时,控制控制试验(RCTs)代表着一种金质标准。然而,RCTs往往范围狭窄,缺乏关于更广大感兴趣人群的数据。这些人群中的因果效应往往使用观测数据集来估计,这些数据集可能存在未观察到的混乱和选择偏差。根据一套观测估计数(例如多份研究),我们提出一个元数,试图拒绝偏差的观测估计数。我们这样做时使用了验证效果、可以从RCT和观察数据中推断出因果关系效应。在拒绝未通过这一测试的估测器之后,我们往往使用观察数据集来估计这些人群中的因果效应,而这些数据集中可能存在不明显混淆和选择偏差的偏差影响。假设至少有一个观测估计值是随机正常的,而且与验证和推断效应一致。我们用算法来保证间隔输出的概率的概率概率概率。为了便利在有必要通过RCT和观察数据集进行因果关系传输的环境下进行假设测试。在拒绝通过这一测试后,我们给在RCT没有观察到的分组中未观察到的分组中观察到的亚因外因效应的外因果影响产生保守信任间隔值,因此,我们用了一种对正常数据分析模型分析方法来显示世界平均分析方法的特性的特性,我们所用的方法。我们使用时,我们用Set分析方法来显示正常分析方法对正常分析方法对正常分析方法对世界分析结果的特性的比。</s>