Causal-Aware Foundation-Model for Bilevel Optimization in Discrete Choice Settings

TL;DR

This paper presents C3PO, a causal-aware foundation model for real-time bilevel optimization in discrete choice settings, improving pricing strategies across various industries.

Contribution

It introduces a novel framework integrating imitation, multi-task, and in-context learning for personalized, constrained pricing optimization in discrete choice environments.

Findings

C3PO outperforms baseline models in simulated and real-world datasets.

The model achieves significant KPI improvements in healthcare, airline, and tender pricing.

Enhanced elasticity priors improve pricing effectiveness for new products.

Abstract

We introduce a causal aware foundation-model framework for real time optimal decision making in discrete choice environments. We propose a constrained triple-head price optimization (C3PO) network to solve a bilevel decision problem in which a service provider selects an optimal assortment while heterogeneous users make personalized acceptance or rejection choices optimizing their own personalized preferences. C3PO integrates imitation learning of prices, multi-task learning of revenue responses, and in context learning of price elasticity to generate pricing recommendations while adhering to business constraints. During inference, frontier model prompting retrieves an enhanced elasticity prior for new products from behavioral economics literature, improving pricing effectiveness. We demonstrate strong in context learning performance using simulated, synthetic, and real-world datasets. C3PO is trained on simulated data generated from multiple classical discrete choice models in economics. The model is trained on data comprising simulated customer segments and counterfactual action and outcome pairs and evaluated on randomly generated choice environments with no access to the underlying preference structure. The trained model consistently improves the pricing KPIs, with gains increasing as customer price sensitivity increases. We also deploy the tuned foundation model for optimal pricing in real-world applications such as healthcare, tender pricing, airline ancillary pricing, and other domains, achieving substantial gains across multiple products, markets, and divisions.