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Observational studies often benefit from an abundance of observational units.This can lead to studies that -- while challenged by issues of internalvalidity -- have inferences derived from sample sizes substantially larger thanrandomized controlled trials. But is the information provided by anobservational unit best used in the analysis phase? We propose the use of`pilot design,' in which observations are expended in the design phase of thestudy, and the post-treatment information from these observations is used toimprove study design. In modern observational studies, which are data rich butcontrol poor, pilot designs can be used to gain information about the structureof post-treatment variation. This information can then be used to improveinstrumental variable designs, propensity score matching, doubly-robustestimation, and other observational study designs.  We illustrate one version of a pilot design, which aims to reduce within-setheterogeneity and improve performance in sensitivity analyses. This version ofa pilot design expends observational units during the design phase to fit aprognostic model, avoiding concerns of overfitting. Additionally, it enablesthe construction of `Assignment-Control (AC) plots,' which visualize therelationship between propensity and prognostic scores. We first show someexamples of these plots, then we demonstrate in a simulation setting how thisalternative use of the observations can lead to gains in terms of bothtreatment effect estimation and sensitivity analyses of unobserved confounding.

A Pilot Design for Observational Studies: Using Abundant Data Thoughtfully