Decision Focused Causal Learning for Direct Counterfactual Marketing Optimization

• URL: http://arxiv.org/abs/2407.13664v1
• Date: Thu, 18 Jul 2024 16:39:44 GMT
• Title: Decision Focused Causal Learning for Direct Counterfactual Marketing Optimization
• Authors: Hao Zhou, Rongxiao Huang, Shaoming Li, Guibin Jiang, Jiaqi Zheng, Bing Cheng, Wei Lin,
• Abstract summary: Decision Focused Learning (DFL) integrates machine learning (ML) and optimization into an end-to-end framework. However, deploying DFL in marketing is non-trivial due to multiple technological challenges. We propose a decision focused causal learning framework (DFCL) for direct counterfactual marketing.
• Score: 21.304040539486184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Marketing optimization plays an important role to enhance user engagement in online Internet platforms. Existing studies usually formulate this problem as a budget allocation problem and solve it by utilizing two fully decoupled stages, i.e., machine learning (ML) and operation research (OR). However, the learning objective in ML does not take account of the downstream optimization task in OR, which causes that the prediction accuracy in ML may be not positively related to the decision quality. Decision Focused Learning (DFL) integrates ML and OR into an end-to-end framework, which takes the objective of the downstream task as the decision loss function and guarantees the consistency of the optimization direction between ML and OR. However, deploying DFL in marketing is non-trivial due to multiple technological challenges. Firstly, the budget allocation problem in marketing is a 0-1 integer stochastic programming problem and the budget is uncertain and fluctuates a lot in real-world settings, which is beyond the general problem background in DFL. Secondly, the counterfactual in marketing causes that the decision loss cannot be directly computed and the optimal solution can never be obtained, both of which disable the common gradient-estimation approaches in DFL. Thirdly, the OR solver is called frequently to compute the decision loss during model training in DFL, which produces huge computational cost and cannot support large-scale training data. In this paper, we propose a decision focused causal learning framework (DFCL) for direct counterfactual marketing optimization, which overcomes the above technological challenges. Both offline experiments and online A/B testing demonstrate the effectiveness of DFCL over the state-of-the-art methods. Currently, DFCL has been deployed in several marketing scenarios in Meituan, one of the largest online food delivery platform in the world.

Related papers

• Learning Constrained Optimization with Deep Augmented Lagrangian Methods [54.22290715244502]
  A machine learning (ML) model is trained to emulate a constrained optimization solver. This paper proposes an alternative approach, in which the ML model is trained to predict dual solution estimates directly. It enables an end-to-end training scheme is which the dual objective is as a loss function, and solution estimates toward primal feasibility, emulating a Dual Ascent method.
  arXiv  Detail & Related papers  (2024-03-06T04:43:22Z)
• Locally Convex Global Loss Network for Decision-Focused Learning [0.5242869847419834]
  Decision-focused Learning (DFL) is a task-oriented framework to integrate prediction and optimization. We propose LCGLN, a global surrogate loss model which can be implemented in general DFL.
  arXiv  Detail & Related papers  (2024-03-04T09:31:56Z)
• On Leveraging Large Language Models for Enhancing Entity Resolution: A Cost-efficient Approach [7.996010840316654]
  We propose an uncertainty reduction framework using Large Language Models (LLMs) to improve entity resolution results. LLMs capitalize on their advanced linguistic capabilities and a pay-as-you-go'' model that provides significant advantages to those without extensive data science expertise. We show that our method is efficient and effective, offering promising applications in real-world tasks.
  arXiv  Detail & Related papers  (2024-01-07T09:06:58Z)
• Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning [54.682106515794864]
  offline reinforcement learning (RL) aims to find a near-optimal policy using pre-collected datasets. This paper introduces $textbfLanguage Models for $textbfMo$tion Control ($textbfLaMo$), a general framework based on Decision Transformers to use pre-trained Language Models (LMs) for offline RL. Empirical results indicate $textbfLaMo$ achieves state-of-the-art performance in sparse-reward tasks.
  arXiv  Detail & Related papers  (2023-10-31T16:24:17Z)
• Model-based Constrained MDP for Budget Allocation in Sequential Incentive Marketing [28.395877073390434]
  Sequential incentive marketing is an important approach for online businesses to acquire customers, increase loyalty and boost sales. How to effectively allocate the incentives so as to maximize the return under the budget constraint is less studied in the literature. We propose an efficient learning algorithm which combines bisection search and model-based planning.
  arXiv  Detail & Related papers  (2023-03-02T08:10:45Z)
• Online Learning under Budget and ROI Constraints via Weak Adaptivity [57.097119428915796]
  Existing primal-dual algorithms for constrained online learning problems rely on two fundamental assumptions. We show how such assumptions can be circumvented by endowing standard primal-dual templates with weakly adaptive regret minimizers. We prove the first best-of-both-worlds no-regret guarantees which hold in absence of the two aforementioned assumptions.
  arXiv  Detail & Related papers  (2023-02-02T16:30:33Z)
• Direct Heterogeneous Causal Learning for Resource Allocation Problems in Marketing [20.9377115817821]
  Marketing is an important mechanism to increase user engagement and improve platform revenue. Most decision-making problems in marketing can be formulated as resource allocation problems and have been studied for decades. Existing works usually divide the solution procedure into two fully decoupled stages, i.e., machine learning (ML) and operation research (OR)
  arXiv  Detail & Related papers  (2022-11-28T19:27:34Z)
• Expert-Calibrated Learning for Online Optimization with Switching Costs [28.737193318136725]
  We study online convex optimization with switching costs. By tapping into the power of machine learning (ML) baseds, ML-augmented online algorithms have been emerging as state of the art. We propose EC-L2O (expert-calibrated learning to optimize), which trains an ML-based algorithm by explicitly taking into account the expert calibrator.
  arXiv  Detail & Related papers  (2022-04-18T21:54:33Z)
• Contingency-Aware Influence Maximization: A Reinforcement Learning Approach [52.109536198330126]
  influence (IM) problem aims at finding a subset of seed nodes in a social network that maximize the spread of influence. In this study, we focus on a sub-class of IM problems, where whether the nodes are willing to be the seeds when being invited is uncertain, called contingency-aware IM. Despite the initial success, a major practical obstacle in promoting the solutions to more communities is the tremendous runtime of the greedy algorithms.
  arXiv  Detail & Related papers  (2021-06-13T16:42:22Z)
• Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement [56.40587594647692]
  We propose a novel transfer learning algorithm, introducing the idea of Target-awareness REpresentation Disentanglement (TRED) TRED disentangles the relevant knowledge with respect to the target task from the original source model and used as a regularizer during fine-tuning the target model. Experiments on various real world datasets show that our method stably improves the standard fine-tuning by more than 2% in average.
  arXiv  Detail & Related papers  (2020-10-16T17:45:08Z)
• Learning What to Defer for Maximum Independent Sets [84.00112106334655]
  We propose a novel DRL scheme, coined learning what to defer (LwD), where the agent adaptively shrinks or stretch the number of stages by learning to distribute the element-wise decisions of the solution at each stage. We apply the proposed framework to the maximum independent set (MIS) problem, and demonstrate its significant improvement over the current state-of-the-art DRL scheme.
  arXiv  Detail & Related papers  (2020-06-17T02:19:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.