Randomized Controlled Trials (RCT)s are relied upon to assess new treatments, but suffer from limited power to guide personalized treatment decisions. On the other hand, observational (i.e., non-experimental) studies have large and diverse populations, but are prone to various biases (e.g. residual confounding). To safely leverage the strengths of observational studies, we focus on the problem of falsification, whereby RCTs are used to validate causal effect estimates learned from observational data. In particular, we show that, given data from both an RCT and an observational study, assumptions on internal and external validity have an observable, testable implication in the form of a set of Conditional Moment Restrictions (CMRs). Further, we show that expressing these CMRs with respect to the causal effect, or "causal contrast", as opposed to individual counterfactual means, provides a more reliable falsification test. In addition to giving guarantees on the asymptotic properties of our test, we demonstrate superior power and type I error of our approach on semi-synthetic and real world datasets. Our approach is interpretable, allowing a practitioner to visualize which subgroups in the population lead to falsification of an observational study.

翻译：随机对照试验（RCT）被广泛用于评估新疗法，但其指导个性化治疗决策的能力有限。相比之下，观察性（即非实验性）研究拥有大规模且多样化的受试人群，但易受各种偏倚（如残余混杂）的影响。为安全利用观察性研究的优势，我们聚焦于证伪问题——即利用RCT验证从观察性数据中习得的因果效应估计。具体而言，我们证明：在同时拥有RCT与观察性研究数据的情况下，关于内部有效性与外部有效性的假设会生成一组可观测、可检验的条件矩约束（CMRs）。进一步，我们表明：相较于个体反事实均值，以因果效应（即“因果对比”）形式表达这些CMR能提供更可靠的证伪检验。除给出检验渐近性质的保证外，我们在半合成数据集与真实世界数据集上验证了该方法在统计检验效力与一类错误控制上的优越性。该方法具有可解释性，能够帮助实践者可视化导致观察性研究被证伪的特定人群亚组。