Efficiently allocating treatments with a budget constraint constitutes an
important challenge across various domains. In marketing, for example, the use
of promotions to target potential customers and boost conversions is limited by
the available budget. While much research focuses on estimating causal effects,
there is relatively limited work on learning to allocate treatments while
considering the operational context. Existing methods for uplift modeling or
causal inference primarily estimate treatment effects, without considering how
this relates to a profit maximizing allocation policy that respects budget
constraints. The potential downside of using these methods is that the
resulting predictive model is not aligned with the operational context.
Therefore, prediction errors are propagated to the optimization of the budget
allocation problem, subsequently leading to a suboptimal allocation policy. We
propose an alternative approach based on learning to rank. Our proposed
methodology directly learns an allocation policy by prioritizing instances in
terms of their incremental profit. We propose an efficient sampling procedure
for the optimization of the ranking model to scale our methodology to
large-scale data sets. Theoretically, we show how learning to rank can maximize
the area under a policy's incremental profit curve. Empirically, we validate
our methodology and show its effectiveness in practice through a series of
experiments on both synthetic and real-world data.

通过学习排名的方法，我们提出了一种在预算约束下有效分配治疗的方法，并验证了其在合成和真实数据上的有效性。