Beyond Cost Sensitivity: Outcome-Aware Off-Policy Evaluation via Local Disclosure

We study off-policy evaluation under local disclosure when policies attach fixed fees or subsidies to strategic actions. Ratio-only disclosure groups applicants with the same cost sensitivity $\alpha =\Gamma /V$, although their absolute stakes and default risks can differ. We characterize when this grouping retains enough information for evaluation. For zero-price targets, covered outcome averages by action and $\alpha$ suffice. For a priced target, two worlds can agree under every zero-transfer experiment over a fixed library yet have default risks zero and one. We use randomized cardinal anchors to label outcome-relevant $(V,\Gamma )$ cells. A common shift of the displayed transfers preserves choices and matches the focal transfer to the target. Horvitz-Thompson moments then consistently estimate approval, risk, and value. One frozen log also supports uniform evaluation of a finite predeclared policy class after data-dependent selection. In the coarse independent experiment, direct labels improve the minimax mean squared error from $\Theta ((Np{g}^2{)}^{-1})$ to $\Theta ((Npg{)}^{-1})$. Our budget analysis accounts for recruitment, follow-up, and gross subsidies. A complete priced-menu simulation evaluates eight policies from three logging menus and verifies transfer matching. Further experiments recover the rare-cell rates and test scalar-type insufficiency with public credit covariates.