Improve ROI with Causal Learning and Conformal Prediction

Abstract--In the commercial sphere, such as operations and maintenance, advertising, and marketing recommendations, intelligent decision-making utilizing data mining and neural network technologies is crucial, especially in resource allocation to optimize ROI. This study delves into the Cost-aware Binary Treatment Assignment Problem (C-BTAP) across different industries, with a focus on the state-of-the-art Direct ROI Prediction (DRP) method. A larger area under the curve indicates better performance. Three popular methods have been proposed for tackling C-BTAP: 1) Two-Phase I. First, TPM utilized uplift models, such as In a wide range of commercial activities, intelligent decisionmaking meta-learners [11], [12], causal forests [6], [13]-[15], or neural based on data mining and neural network technologies network based representation learning [16]-[18] approaches, is playing an increasingly important role. One crucial aspect of to predict the revenue lift and cost lift, respectively. Then, this intelligent decision-making is figuring out how to allocate a calculation is performed by dividing the revenue uplift limited resources in order to maximize returns, essentially prediction by the cost uplift prediction. For instance, of revenue uplift model and cost uplift model may cause an in the field of operations and maintenance, how to allocate enlargement of model errors due to the mathematical operations machine resources and computational power to maximize the during combination; 2) For the method of Direct Rank (DR), revenue of supported businesses [1]; in the advertising sector, a loss function aimed at ranking individuals' ROI is created, how to distribute an advertiser's total budget reasonably to as noted in [9]. However, [5] demonstrate that achieving maximize the revenue from their products [2]; and in the accurate ranking is not possible when the loss function fully realms of recommendation and marketing, how to allocate converges because the loss function is not convex, which is suitable coupons, discounts, and coins as incentives to users in also detailed in Appendix E of [5]; 3) based on our research order to maximize platform user retention, GMV, etc [3]-[8]. of the published literature, the Direct ROI Prediction (DRP) In causal inference, actions such as adjusting the computational method [5], presented at AAAI 2023, remains the state-ofthe-art power for a specific business operation, modulating (SOTA) for C-BTAP so far. DRP designs a convex the cost of a particular advertisement, and offering incentives loss function for neural networks to guarantee an unbiased of varying value, as mentioned in the above examples, are estimation of ROI of individuals when the loss converges.