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Problem definition: Mining for heterogeneous responses to an intervention isa crucial step for data-driven operations, for instance to personalizetreatment or pricing. We investigate how to estimate price sensitivity fromtransaction-level data. In causal inference terms, we estimate heterogeneoustreatment effects when (a) the response to treatment (here, whether a customerbuys a product) is binary, and (b) treatment assignments are partially observed(here, full information is only available for purchased items).Methodology/Results: We propose a recursive partitioning procedure to estimateheterogeneous odds ratio, a widely used measure of treatment effect in medicineand social sciences. We integrate an adversarial imputation step to allow forrobust estimation even in presence of partially observed treatment assignments.We validate our methodology on synthetic data and apply it to three casestudies from political science, medicine, and revenue management. ManagerialImplications: Our robust heterogeneous odds ratio estimation method is a simpleand intuitive tool to quantify heterogeneity in patients or customers andpersonalize interventions, while lifting a central limitation in many revenuemanagement data.

Robust and Heterogenous Odds Ratio: Estimating Price Sensitivity for Unbought Items