Where to Spend the Intervention Budget: Understanding and Using Preintervention Information

An uncertain decision is worth revisiting only if an alternative action, a repair, can improve its outcome. With a limited budget, an agent must choose which decisions to evaluate before it knows what their alternatives will achieve. We study which available information helps make this choice, measured by Capture: the share of an oracle's repair value that a selection obtains under the same budget. Across four synthetic retrieval settings and seven seeds, features that describe the candidate alternatives yield 0.4266 Capture, compared with 0.2669 for retrieval features when both feed the same value regressor. Predicting whether a useful repair exists reaches 0.5078; nearest neighbor regression on similar, previously evaluated decisions reaches 0.6235. These findings motivate Cue Informed Allocation (CIA), which estimates repair value and whether a repair exists, chooses a scoring rule on previously evaluated decisions, and ranks new decisions for evaluation. At a 20% budget, it reaches 0.6316 Capture, mostly owing to the nearest neighbor estimate. We also compare eight policy learning methods adapted to the same allocation task and examine recorded outcomes from interactive tasks. Candidate features and outcomes of similar decisions account for most of the gain; combining predictions and choosing rules add smaller gains that depend on the setting.