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:依靠随机控制试验(RCT)来评估新的治疗方法,但受限于指导个人化治疗决定的权力有限。另一方面,观察(即非实验性)研究的人口众多,种类繁多,但容易出现各种偏差(例如残余混杂)。为了安全地利用观察研究的长处,我们集中研究伪造问题,利用RCT来验证从观察数据中得出的因果估计;特别是,我们表明,从RCT和观察研究获得的数据来看,关于内部和外部有效性的假设具有可观察和可检验的内外部影响,其形式是一套有条件流动限制(CMR)。此外,我们表明,表达这些CMR(CMR)与个别反事实手段相比,具有因果关系,提供了更可靠的伪造检验标准。除了保证我们测试的无症状特性外,我们还显示了我们在半合成和真实世界观察方法上的超强力和I型错误,其形式是一套可观察到的、可测试的、可测试的、可测试的影响。此外,我们表明,在对可视化的人口进行视觉化研究的分组中,我们可以解释的CMRMR(即“因果学)。</s>