Kernel-based External Validity of Experimental Studies
The external validity of a study is the extent to which its findings generalize to unseen experimental conditions, such as datasets. We formalize the results of a study together with its research question, and define external validity as the probability that two independent and same-sized replications of the study produce results whose maximum mean discrepancy (MMD) is below a given tolerance. This approach, centered on the choice of a space of results and of an appropriate reproducing kernel for the MMD, is flexible enough to be deployed on multiple different types of results and research questions. Additionally, we establish an asymptotic power-law relation between the size of a study a...
| Authors | Federico Matteucci, Gerhard Neumann |
|---|---|
| Primary category | stat.ME |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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