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。

用于排序治疗效果的元学习

Metalearners for Ranking Treatment Effects

We present a multi-agent learning algorithm, ALMA-Learning, for efficient and
fair allocations in large-scale systems. We circumvent the traditional pitfalls
of multi-agent learning (e.g., the moving target problem, the curse of
dimensionality, or the need for mutually consistent actions) by relying on the
ALMA heuristic as a coordination mechanism for each stage game. ALMA-Learning
is decentralized, observes only own action/reward pairs, requires no
inter-agent communication, and achieves near-optimal (<5% loss) and fair
coordination in a variety of synthetic scenarios and a real-world meeting
scheduling problem. The lightweight nature and fast learning constitute
ALMA-Learning ideal for on-device deployment.

本文提出了一种多智能体学习算法 ALMA-Learning，用于大规模系统的高效和公平分配，该算法通过依赖于 ALMA 启发式协调机制来克服传统多智能体学习的问题，适用于各种情景和实际问题，且具有轻量级和快速学习的特点，适合于设备上的部署。