Learning under counterfactual shifts in image acquisition

A change in image acquisition can alter the optimal image-only predictor even when class proportions and the overall image distribution remain unchanged, because the class probabilities associated with a given image can change under the deployed policy. We study classification under a prespecified change in the acquisition policy using historical data alone. The intervention preserves historical assignment support, avoiding an additional assignment-positivity requirement. Under conditional exchangeability and missing-at-random labels, we identify the target Brier risk and its Bayes predictor and derive an efficient-influence-function-corrected training criterion. With linear conditional-risk regression on metadata, the criterion reduces exactly to a Brier objective with precomputed record weights, enabling gradient-based training without repeated regression fitting. We establish a uniform excess-risk bound with product-form nuisance errors and a squared propensity- error term. Simulations and a semi-synthetic Camelyon17 study show that targeting the planned policy provides most of the improvement, while the additional benefit of correction varies with the predictor and sample size.