  Participation incentives a well-known issue inhibiting randomized clinical
trials (RCTs). We frame this issue as a non-standard exploration-exploitation
tradeoff: an RCT would like to explore as uniformly as possible, whereas each
patient prefers "exploitation", i.e., treatments that seem best. We incentivize
participation by leveraging information asymmetry between the trial and the
patients. We measure statistical performance via worst-case estimation error
under adversarially generated outcomes, a standard objective for RCTs. We
obtain a near-optimal solution in terms of this objective: an
incentive-compatible mechanism with a particular guarantee, and a nearly
matching impossibility result for any incentive-compatible mechanism. We
consider three model variants: homogeneous patients (of the same "type"
comprising preferences and medical histories), heterogeneous agents, and an
extension with estimated type frequencies.
