Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions
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 holds only within subsets of interventions that leave a target variable "unaffected" (e.g., non-descendants of an intervened node in a causal graph). We study the practical regime where this invariance structure is unknown and must be estimated from data. Our main result quantifies how coverage degrades when the estimated safe calibration set accidentally includes interventions that affect the target, and gives a conservative corr...
| Authors | Amir Asiaee, Kavey Aryan, James P. Long |
|---|---|
| Primary category | cs.LG |
| Published | 2 March 2026 |
| Last updated at source | 9 July 2026 |
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