Offering incentives (e.g., coupons at Amazon, discounts at Uber and video
bonuses at Tiktok) to user is a common strategy used by online platforms to
increase user engagement and platform revenue. Despite its proven
effectiveness, these marketing incentives incur an inevitable cost and might
result in a low ROI (Return on Investment) if not used properly. On the other
hand, different users respond differently to these incentives, for instance,
some users never buy certain products without coupons, while others do anyway.
Thus, how to select the right amount of incentives (i.e. treatment) to each
user under budget constraints is an important research problem with great
practical implications. In this paper, we call such problem as a
budget-constrained treatment selection (BTS) problem.
The challenge is how to efficiently solve BTS problem on a Large-Scale
dataset and achieve improved results over the existing techniques. We propose a
novel tree-based treatment selection technique under budget constraints, called
Large-Scale Budget-Constrained Causal Forest (LBCF) algorithm, which is also an
efficient treatment selection algorithm suitable for modern distributed
computing systems. A novel offline evaluation method is also proposed to
overcome an intrinsic challenge in assessing solutions' performance for BTS
problem in randomized control trials (RCT) data. We deploy our approach in a
real-world scenario on a large-scale video platform, where the platform gives
away bonuses in order to increase users' campaign engagement duration. The
simulation analysis, offline and online experiments all show that our method
outperforms various tree-based state-of-the-art baselines. The proposed
approach is currently serving over hundreds of millions of users on the
platform and achieves one of the most tremendous improvements over these
months.

在大型在线平台上，提供刺激（例如亚马逊优惠券、优步折扣和抖音视频奖励）以增加用户参与和平台收入是一种常见策略，然而如何在预算限制下为每个用户选择适当金额的刺激成为一个有实际意义的研究问题。本文提出了一种基于树的、适用于现代分布式计算系统的大规模预算约束因果森林（LBCF）算法，以及一种新的离线评估方法来解决这个问题。在对一个大型视频平台的实际场景进行模拟分析、离线和在线实验后，这种方法表现出超过各种基于树的最先进基线的优越性，并服务于数亿用户达多个月之久。