When Can Experiments Be Reused? Certified Transport, Refusal, and Causal Bridging

Past experiments can help answer new causal questions, but similar study reports do not guarantee that their results apply to a new setting. Differences in populations, control conditions, or exposure can change treatment effects even when past estimates are precise. We develop a framework for deciding when experimental results can be reused. It represents uncertainty about these differences as sets and combines this uncertainty with sampling error to bound the error of a target estimate. The bound remains valid even when studies and their weights are selected after observing their results. The framework releases an estimate only when its error bound meets a prespecified tolerance. Otherwise, it reports a range of plausible effects and considers additional experiments. We prove the bound’s validity under stated assumptions, characterize what an archive cannot identify, and establish guarantees for choosing additional experiments under a restricted linear-Gaussian model. Controlled simulations show why both mechanism uncertainty and selection must be accounted for: after adaptive selection among 128 sources, simultaneous protection achieves 95.3% coverage, compared with 0.2% for unadjusted intervals. A Many Labs 2 benchmark using meta-analytic baselines illustrates how tighter precision requirements reduce the fraction of targets eligible for release. Together, these results support explicit reuse and refusal decisions, while independent calibration of mechanism uncertainty remains necessary for applications across different interventions.