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：随机对照试验被用于评估新疗法，但在指导个性化治疗决策方面存在效力不足的局限。相比之下，观察性（即非实验性）研究拥有规模庞大且异质性高的人群，但容易受到各种偏倚（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——即利用随机对照试验验证从观察数据中习得的因果效应估计。具体而言，我们证明：给定随机对照试验与观察性研究数据后，关于内外部有效性的假设可通过一组条件矩约束转化为可观测、可检验的推论。此外，我们进一步表明：基于因果效应（即"因果对照"）而非个体反事实均值来表达这些条件矩约束，能够提供更可靠的证伪检验。除给出检验渐近性质的保障外，我们在半合成数据集和真实世界数据集上验证了该方法在检验效能与第一类错误控制方面的优越性。该方法具有可解释性，能帮助研究者直观识别导致观察性研究证伪的特定人群亚组。