Harnessing Heterogeneous Data for Conditional Optimization via Optimal Transport

Conditional optimization tailors decisions to contextual or event information, but its practical use is often limited by the difficulty of learning the relevant conditional distribution from finite samples of a target joint distribution.

This challenge is especially acute when target joint data are scarce or unavailable, or when few observations fall in the conditioning region of interest.

Related joint data may be available from multiple sources, such as different stores, markets, populations, or operating environments, but these sources may be biased relative to the target distribution and cannot be pooled naively.

We develop a distributionally robust framework based on optimal transport (OT) for harnessing such heterogeneous data in conditional optimization.

The framework constructs ambiguity sets over joint distributions using OT distances to empirical source distributions and optimizes worst-case conditional performance over plausible target laws.

We propose three OT ambiguity sets that capture different ways of using heterogeneous sources: enforcing simultaneous source consistency, aggregating source discrepancies through weights, and centering the ambiguity set at an OT barycenter.

We derive tractable reformulations, establish feasibility conditions, discuss parameter choices, and characterize the relationships among the formulations, revealing trade-offs between robustness, information aggregation, and computational complexity.

We demonstrate the value of the framework through a conditional assortment problem using demand and product-feature data from multiple stores.