Robust and Heterogenous Odds Ratio: Estimating Price Sensitivity for Unbought Items
提出一种递归划分方法估计异质比值比，通过对抗性插补处理交易数据中部分缺失的处理分配，用于量化顾客或患者的异质性并个性化干预，适用于收益管理、医学等领域。
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. History: This paper has been accepted as part of the 2020 MSOM Data Driven Research Challenge. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1118 .