LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm

Meng Ai · Biao Li · Heyang Gong · Qingwei Yu · Yunzhou Zhang · Peng Jiang

24 citations0/0 files storedInternational World Wide Web Conference (WWW)

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Advanced Bandit Algorithms Research Recommender Systems and Techniques Advanced Causal Inference Techniques

Abstract

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.

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2015From Infrastructure to Culture 2011Decision trees for uplift modeling with single and multiple treatments 2006A dynamic segmentation approach for targeting and customizing direct marketing campaigns 2012Real-World Uplift Modelling with Significance-Based Uplift Trees 2014Ensemble methods for uplift modeling 2007Using control groups to target on predicted lift: Building and assessing uplift model 2019A Unified Framework for Marketing Budget Allocation 2017Monetary Discount Strategies for Real-Time Promotion Campaign

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2023Direct Heterogeneous Causal Learning for Resource Allocation Problems in Marketing

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