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Results from randomized experiments (trials) can be severely distorted byoutcome misclassification, such as from measurement error or reporting bias inbinary outcomes. All existing approaches to outcome misclassification rely onsome data-generating (super-population) model and therefore may not beapplicable to randomized experiments without additional assumptions. We proposea model-free and finite-population-exact framework for randomized experimentssubject to outcome misclassification. A central quantity in our framework is"warning accuracy," defined as the threshold such that the causal conclusiondrawn from the measured outcomes may differ from that based on the trueoutcomes if the outcome measurement accuracy did not surpass that threshold. Weshow how learning the warning accuracy and related concepts can benefit arandomized experiment subject to outcome misclassification. We show that thewarning accuracy can be computed efficiently (even for large datasets) byadaptively reformulating an integer program with respect to the randomizationdesign. Our framework covers both Fisher's sharp null and Neyman's weak null,works for a wide range of randomization designs, and can also be applied toobservational studies adopting randomization-based inference. We apply ourframework to a large randomized clinical trial for the prevention of prostatecancer.

A Model-Free and Finite-Population-Exact Framework for Randomized Experiments Subject to Outcome Misclassification via Integer Programming