Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation

Effective personalized incentives can improve user experience and increase platform revenue, resulting in a win-win situation between users and e-commerce companies. Previous studies have used uplift modeling methods to estimate the conditional average treatment effects of users' incentives, and then placed the incentives by maximizing the sum of estimated treatment effects under a limited budget. However, some users will always buy whether incentives are given or not, and they will actively collect and use incentives if provided, named "Always Buyers". Identifying and predicting these "Always Buyers" and reducing incentive delivery to them can lead to a more rational incentive allocation. In this paper, we first divide users into five strata from an individual counterfactual perspective, and reveal the failure of previous uplift modeling methods to identify and predict the "Always Buyers". Then, we propose principled counterfactual identification and estimation methods and prove their unbiasedness. We further propose a counterfactual entire-space multi-task learning approach to accurately perform personalized incentive policy learning with a limited budget. We also theoretically derive a lower bound on the reward of the learned policy. Extensive experiments are conducted on three real-world datasets with two common incentive scenarios, and the results demonstrate the effectiveness of the proposed approaches.