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

Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions