# Explicit Feature Interaction-aware Uplift Network for Online Marketing

arXiv: https://arxiv.org/abs/2306.00315 · HTML: https://mlsanity.com/paper/2306.00315/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 40 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 23 of 23)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Causal Effect Inference with Deep Latent-Variable Models](https://mlsanity.com/paper/1705.08821/) (2017)
- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://mlsanity.com/paper/2008.13535/) (2020)
- [Adapting Neural Networks for the Estimation of Treatment Effects](https://mlsanity.com/paper/1906.02120/) (2019)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms](https://mlsanity.com/paper/2101.10943/) (2021)
- [On Inductive Biases for Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2106.03765/) (2021)
- [Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction](https://mlsanity.com/paper/1910.05552/) (2019)
- [Uplift Modeling with Multiple Treatments and General Response Types](https://mlsanity.com/paper/1705.08492/) (2017)
- [A unified survey of treatment effect heterogeneity modeling and uplift modeling](https://mlsanity.com/paper/2007.12769/) (2020)
- [A Unified Framework for Marketing Budget Allocation](https://mlsanity.com/paper/1902.01128/) (2019)
- [E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling](https://mlsanity.com/paper/2108.13298/) (2021)
- [Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints](https://mlsanity.com/paper/2008.06293/) (2020)
- [Learning to rank for uplift modeling](https://mlsanity.com/paper/2002.05897/) (2020)
- [LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm](https://mlsanity.com/paper/2201.12585/) (2022)
- [DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation](https://mlsanity.com/paper/2207.09920/) (2022)
- [Optimizing Feature Set for Click-Through Rate Prediction](https://mlsanity.com/paper/2301.10909/) (2023)
- [Memorize, Factorize, or be Na"ive: Learning Optimal Feature Interaction Methods for CTR Prediction](https://mlsanity.com/paper/2108.01265/) (2021)
- [A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling](https://mlsanity.com/paper/2111.10106/) (2021)
- [GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace](https://mlsanity.com/paper/2203.10975/) (2022)

## Built on by (most cited first, 25 of 25)

- [End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling](https://mlsanity.com/paper/2408.11623/) (2024)
- [Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing](https://mlsanity.com/paper/2402.03379/) (2024)
- [M3TN: Multi-gate Mixture-of-Experts based Multi-valued Treatment Network for Uplift Modeling](https://mlsanity.com/paper/2401.14426/) (2024)
- [Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization](https://mlsanity.com/paper/2310.04693/) (2023)
- [Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation](https://mlsanity.com/paper/2312.05276/) (2023)
- [Multi-Treatment Multi-Task Uplift Modeling for Enhancing User Growth](https://mlsanity.com/paper/2408.12803/) (2024)
- [Benchmarking for Deep Uplift Modeling in Online Marketing](https://mlsanity.com/paper/2406.00335/) (2024)
- [iLOCO: Distribution-Free Inference for Feature Interactions](https://mlsanity.com/paper/2502.06661/) (2025)
- [Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing](https://mlsanity.com/paper/2502.15697/) (2025)
- [Ads Supply Personalization via Doubly Robust Learning](https://mlsanity.com/paper/2410.12799/) (2024)
- [Session-Level Dynamic Ad Load Optimization using Offline Robust Reinforcement Learning](https://mlsanity.com/paper/2501.05591/) (2025)
- [MMCE: A Framework for Deep Monotonic Modeling of Multiple Causal Effects](https://mlsanity.com/paper/2504.03753/) (2025)
- [Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework](https://mlsanity.com/paper/2602.12972/) (2026)
- [Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness](https://mlsanity.com/paper/2603.20775/) (2026)
- [TSCAN: Context-Aware Uplift Modeling via Two-Stage Training for Online Merchant Business Diagnosis](https://mlsanity.com/paper/2504.18881/) (2025)
- [The Estimation of Continual Causal Effect for Dataset Shifting Streams](https://mlsanity.com/paper/2504.20471/) (2025)
- [Which Company Adjustment Matter? Insights from Uplift Modeling on Financial Health](https://mlsanity.com/paper/2506.19049/) (2025)
- [KLAN: Kuaishou Landing-page Adaptive Navigator](https://mlsanity.com/paper/2507.23459/) (2025)
- [Multi-Treatment-DML: Causal Estimation for Multi-Dimensional Continuous Treatments with Monotonicity Constraints in Personal Loan Risk Optimization](https://mlsanity.com/paper/2508.02183/) (2025)
- [A Comparative Study of Model Adaptation Strategies for Multi-Treatment Uplift Modeling](https://mlsanity.com/paper/2511.01185/) (2025)
- [Orthogonal Uplift Learning with Permutation-Invariant Representations for Combinatorial Treatments](https://mlsanity.com/paper/2602.19851/) (2026)
- [VALOR: Value-Aware Revenue Uplift Modeling with Treatment-Gated Representation for B2B Sales](https://mlsanity.com/paper/2604.02472/) (2026)
- [Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding](https://mlsanity.com/paper/2606.27114/) (2026)
- [FunnelCausalNet: Funnel-aware Joint Conversion-Revenue Uplift for Multi-tier Coupon Allocation](https://mlsanity.com/paper/2608.11675/) (2026)
- [Doubly Robust Estimation of Causal Effect on CVR with Targeted Regularization](https://mlsanity.com/paper/2608.13461/) (2026)

## Abstract (arXiv)

As a key component in online marketing, uplift modeling aims to accurately capture the degree to which different treatments motivate different users, such as coupons or discounts, also known as the estimation of individual treatment effect (ITE). In an actual business scenario, the options for treatment may be numerous and complex, and there may be correlations between different treatments. In addition, each marketing instance may also have rich user and contextual features. However, existing methods still fall short in both fully exploiting treatment information and mining features that are sensitive to a particular treatment. In this paper, we propose an explicit feature interaction-aware uplift network (EFIN) to address these two problems. Our EFIN includes four customized modules: 1) a feature encoding module encodes not only the user and contextual features, but also the treatment features; 2) a self-interaction module aims to accurately model the user's natural response with all but the treatment features; 3) a treatment-aware interaction module accurately models the degree to which a particular treatment motivates a user through interactions between the treatment features and other features, i.e., ITE; and 4) an intervention constraint module is used to balance the ITE distribution of users between the control and treatment groups so that the model would still achieve a accurate uplift ranking on data collected from a non-random intervention marketing scenario. We conduct extensive experiments on two public datasets and one product dataset to verify the effectiveness of our EFIN. In addition, our EFIN has been deployed in a credit card bill payment scenario of a large online financial platform with a significant improvement.