# Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing

arXiv: https://arxiv.org/abs/2402.03379 · HTML: https://mlsanity.com/paper/2402.03379/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 4 Feb 2024.

## Builds on (most cited first, 16 of 16)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [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)
- [Adapting Neural Networks for the Estimation of Treatment Effects](https://mlsanity.com/paper/1906.02120/) (2019)
- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://mlsanity.com/paper/1804.07931/) (2018)
- [NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction](https://mlsanity.com/paper/1801.08297/) (2018)
- [Learning to Branch for Multi-Task Learning](https://mlsanity.com/paper/2006.01895/) (2020)
- [Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks](https://mlsanity.com/paper/2002.12326/) (2020)
- [CausalML: Python Package for Causal Machine Learning](https://mlsanity.com/paper/2002.11631/) (2020)
- [PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://mlsanity.com/paper/2302.01115/) (2023)
- [Automated Search for Resource-Efficient Branched Multi-Task Networks](https://mlsanity.com/paper/2008.10292/) (2020)
- [A Unified Framework for Marketing Budget Allocation](https://mlsanity.com/paper/1902.01128/) (2019)
- [Explicit Feature Interaction-aware Uplift Network for Online Marketing](https://mlsanity.com/paper/2306.00315/) (2023)
- [Enhancing CTR Prediction with Context-Aware Feature Representation Learning](https://mlsanity.com/paper/2204.08758/) (2022)
- [DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation](https://mlsanity.com/paper/2207.09920/) (2022)

## Built on by (most cited first, 7 of 7)

- [Multi-Treatment Multi-Task Uplift Modeling for Enhancing User Growth](https://mlsanity.com/paper/2408.12803/) (2024)
- [Improve ROI with Causal Learning and Conformal Prediction](https://mlsanity.com/paper/2407.01065/) (2024)
- [SACO: Sequence-Aware Constrained Optimization Framework for Coupon Distribution in E-commerce](https://mlsanity.com/paper/2508.09198/) (2025)
- [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)
- [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)

Uplift modeling, vital in online marketing, seeks to accurately measure the impact of various strategies, such as coupons or discounts, on different users by predicting the Individual Treatment Effect (ITE). In an e-commerce setting, user behavior follows a defined sequential chain, including impression, click, and conversion. Marketing strategies exert varied uplift effects at each stage within this chain, impacting metrics like click-through and conversion rate. Despite its utility, existing research has neglected to consider the inter-task across all stages impacts within a specific treatment and has insufficiently utilized the treatment information, potentially introducing substantial bias into subsequent marketing decisions. We identify these two issues as the chain-bias problem and the treatment-unadaptive problem. This paper introduces the Entire Chain UPlift method with context-enhanced learning (ECUP), devised to tackle these issues. ECUP consists of two primary components: 1) the Entire Chain-Enhanced Network, which utilizes user behavior patterns to estimate ITE throughout the entire chain space, models the various impacts of treatments on each task, and integrates task prior information to enhance context awareness across all stages, capturing the impact of treatment on different tasks, and 2) the Treatment-Enhanced Network, which facilitates fine-grained treatment modeling through bit-level feature interactions, thereby enabling adaptive feature adjustment. Extensive experiments on public and industrial datasets validate ECUPs effectiveness. Moreover, ECUP has been deployed on the Meituan food delivery platform, serving millions of daily active users, with the related dataset released for future research.