Beyond a Fixed Composite Endpoint: Guarded Orthogonal Learning for Preference-Robust Target-Group Audits

Clinical trials use a prespecified primary endpoint, yet downstream benefit–risk decisions often combine several outcomes and value trade-offs. We ask whether the treatment conclusion for a prespecified target group remains stable over an admissible utility class. We introduce PROBE, which learns reusable cross-fitted orthogonal effects for interpretable basis outcomes. We compare complete effect vectors under a utility-aware patient-level loss and use a held-out guard to adapt among zero, constant, shared, coordinate-specific, and private structures. The selected vector supports post-fit prediction, whereas confirmatory inference uses cross-fitted basis scores to construct direct multiplier max-$t$ bands over the prespecified group–utility family. These bands drive a four-way audit that returns robust support for either treatment, certified preference sensitivity, or evidence uncertainty. External preference information can then refine the admissible class without rerunning causal estimation. For this pipeline, we establish a uniform basis-approximation bound, an oracle inequality with an explicit exclusion price, false-admission control, asymptotic familywise validity, and an $\alpha +\beta$ error bound combining causal and preference uncertainty. In prespecified raw-data simulations, PROBE admits complex structure in 50/50 strong private and 44/50 strong mixed runs, while all 100 null runs retain an anchor. Two 1,000-run reference experiments attain familywise coverage of 0.945 and 0.949. We then apply the causal audit to the IST randomized trial and analyze dependence-preserving preference evidence using 394 same-respondent part-worth vectors from a tuberculosis discrete-choice study. Together, these results show how PROBE supports transparent target-group auditing across clinically meaningful value trade-offs.