Many quantities of interest are only partially identified from observable data: the data can limit them to a set of plausible values, but not uniquely determine them. This paper develops a framework for covariate-assisted estimation, inference, and decision making in partial identification problems where the parameter of interest satisfies linear constraints conditional on covariates. Bounds on the parameter can be written as expectations of solutions to conditional linear programs with covariate-dependent objectives and constraints that must be estimated from data. Examples include estimands involving joint distributions of potential outcomes, policy learning with inequality-aware value functions, and instrumental variable settings. We propose two de-biased estimators. The first solves the conditional linear programs with plugin estimates and de-biases using standard output from linear program solvers, avoiding computationally demanding vertex enumeration. The second uses entropic regularization to create a smooth approximation, trading a small amount of approximation error for improved estimation and computational efficiency. We establish asymptotic normality for both estimators, show robustness to first-order errors in estimating nuisance functions, construct Wald-type confidence bounds, and extend to policy learning problems where the value of a decision policy is only partially identified. We apply our methods to study the effects of Medicaid enrollment.
