  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.
