Robust and Heterogenous Odds Ratio: Estimating Price Sensitivity for Unbought Items
Journal
Manufacturing & Service Operations Management
Subject
Management Science and Operations
Publishing details
Authors / Editors
Pauphilet J
Biographies
Publication Year
2024
Abstract
Problem definition: Mining for heterogeneous responses to an intervention is a crucial step for data-driven operations, for instance to personalize treatment or pricing. We investigate how to estimate price sensitivity from transaction-level data. In causal inference terms, we estimate heterogeneous treatment effects when (a) the response to treatment (here, whether a customer buys 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 estimate heterogeneous odds ratio, a widely used measure of treatment effect in medicine and social sciences. We integrate an adversarial imputation step to allow for robust estimation even in presence of partially observed treatment assignments. We validate our methodology on synthetic data and apply it to three case studies from political science, medicine, and revenue management. Managerial Implications: Our robust heterogeneous odds ratio estimation method is a simple and intuitive tool to quantify heterogeneity in patients or customers and personalize interventions, while lifting a central limitation in many revenue management data.
Keywords
Prescriptive analytics; Pricing; Customer segmentation; Causal inference; Missing data
Available on ECCH
No
