Support-Aware Counterfactual Analysis

Data-grounded counterfactual methods often retrieve one or a few nearby instances with different outcomes, but finite samples may not reveal whether these instances reflect a recurring local pattern or isolated observations. We introduce Support-Aware Counterfactual Analysis (SACA) to characterize the surrounding evidence and determine which conclusions the data support. SACA estimates a distance–direction landscape of different-outcome probabilities and characterizes the evidence through three perspectives: prevalence measures how common different outcomes are nearby, direction identifies where they concentrate, and reach measures how far one must look to accumulate a prescribed amount of evidence. It reports regional estimates only when finite-sample bounds meet the requested accuracy and otherwise abstains. These support deficits guide acquisition of additional outcomes from a pool of unlabeled cases. We establish a near-optimal rate for landscape estimation, finite-sample guarantees for reach and reported regions, and approximation guarantees for acquisition. Experiments on six real-world datasets and five synthetic settings show that SACA reduces landscape estimation error by 22%, makes about 19% more regions reportable, and selects cases 22 times faster.