arXiv stat.ML
· Papers
Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions
arXiv:2603.02204v2 Announce Type: replace-cross Abstract: Selective conformal prediction can yield substantially tighter uncertainty sets when we can identify calibration examples that are exchangeable with the test example. In interventional settings, such as perturbation experiments in genomics, exchangeability often