mlsanityData to 7 Oct 2026

Improve ROI with Causal Learning and Conformal Prediction

arXiv 2407.01065 ↗First posted 1 Jul 2024cs.LGcited by 3

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Abstract (arXiv)

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. However, the DRP model confronts issues like covariate shift and insufficient training data, hindering its real-world effectiveness. Addressing these challenges is essential for ensuring dependable and robust predictions in varied operational contexts. This paper presents a robust Direct ROI Prediction (rDRP) method, designed to address challenges in real-world deployment of neural network-based uplift models, particularly under conditions of covariate shift and insufficient training data. The rDRP method, enhancing the standard DRP model, does not alter the model's structure or require retraining. It utilizes conformal prediction and Monte Carlo dropout for interval estimation, adapting to model uncertainty and data distribution shifts. A heuristic calibration method, inspired by a Kaggle competition, combines point and interval estimates. The effectiveness of these approaches is validated through offline tests and online A/B tests in various settings, demonstrating significant improvements in target rewards compared to the state-of-the-art method.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 41 of its 61 references that are in the map. First posted on arXiv 1 Jul 2024.

Builds on

41 of 61 references.

References · 41

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  2. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  3. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 813
  4. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 777
  5. 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
  6. 2019Conformalized Quantile Regressioncited by 620
  7. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  8. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  9. 2020Uncertainty Sets for Image Classifiers using Conformal Predictioncited by 365
  10. 2020Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Userscited by 336
  11. 2017Smart "Predict, then Optimize"cited by 332
  12. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 328
29 more
  1. 2016Learning Representations for Counterfactual Inferencecited by 327
  2. 2016Generalized Random Forestscited by 309
  3. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 285
  4. 2021Recent Advances in Adversarial Training for Adversarial Robustnesscited by 276
  5. 2018Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimizationcited by 220
  6. 2020Conformal Inference of Counterfactuals and Individual Treatment Effectscited by 150
  7. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
  8. 2022Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imagingcited by 107
  9. 2017Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competitioncited by 106
  10. 2021On Inductive Biases for Heterogeneous Treatment Effect Estimationcited by 74
  11. 2018Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertisingcited by 65
  12. 2018Offline Multi-Action Policy Learning: Generalization and Optimizationcited by 64
  13. 2021Decision-Focused Learning: Through the Lens of Learning to Rankcited by 61
  14. 2013Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknifecited by 43
  15. 2017Uplift Modeling with Multiple Treatments and General Response Typescited by 41
  16. 2017Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effectscited by 41
  17. 2019A Unified Framework for Marketing Budget Allocationcited by 33
  18. 2022Direct Heterogeneous Causal Learning for Resource Allocation Problems in Marketingcited by 21
  19. 2020Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraintscited by 20
  20. 2022LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithmcited by 17
  21. 2020Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertisingcited by 16
  22. 2023Model-based Constrained MDP for Budget Allocation in Sequential Incentive Marketingcited by 15
  23. 2023An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraintcited by 9
  24. 2021Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approachcited by 8
  25. 2020DCAF: A Dynamic Computation Allocation Framework for Online Serving Systemcited by 7
  26. 2024Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketingcited by 7
  27. 2020Heterogeneous Causal Learning for Effectiveness Optimization in User Marketingcited by 6
  28. 2023Uplift Modeling: from Causal Inference to Personalizationcited by 5
  29. 2023Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitizationcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2026Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Frameworkcited by 1
  2. 2025Hidden Representation Clustering with Multi-Task Representation Learning towards Robust Online Budget Allocationcited by 0
  3. 2025Deep Learning of Continuous and Structured Policies for Aggregated Heterogeneous Treatment Effectscited by 0