Practical Marketplace Optimization at Uber Using Causally-Informed Machine Learning
Abstract
Budget allocation of marketplace levers, such as incentives for drivers and promotions for riders, has long been a technical and business challenge at Uber; understanding lever budget changes' impact and estimating cost efficiency to achieve predefined budgets is crucial, with the goal of optimal allocations that maximize business value; we introduce an end-to-end machine learning and optimization procedure to automate budget decision-making for cities, relying on feature store, model training and serving, optimizers, and backtesting; proposing state-of-the-art deep learning (DL) estimator based on S-Learner and a novel tensor B-Spline regression model, we solve high-dimensional optimization with ADMM and primal-dual interior point convex optimization, substantially improving Uber's resource allocation efficiency.
Cite
@article{arxiv.2407.19078,
title = {Practical Marketplace Optimization at Uber Using Causally-Informed Machine Learning},
author = {Bobby Chen and Siyu Chen and Jason Dowlatabadi and Yu Xuan Hong and Vinayak Iyer and Uday Mantripragada and Rishabh Narang and Apoorv Pandey and Zijun Qin and Abrar Sheikh and Hongtao Sun and Jiaqi Sun and Matthew Walker and Kaichen Wei and Chen Xu and Jingnan Yang and Allen T. Zhang and Guoqing Zhang},
journal= {arXiv preprint arXiv:2407.19078},
year = {2024}
}
Comments
To be published in the 2nd Workshop on Causal Inference and Machine Learning in Practice, KDD 2024, August 25 to 29, 2024, Barcelona, Spain, 10 pages