# A unified survey of treatment effect heterogeneity modeling and uplift modeling

arXiv: https://arxiv.org/abs/2007.12769 · HTML: https://mlsanity.com/paper/2007.12769/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 30 of 36)

- [Explicit Feature Interaction-aware Uplift Network for Online Marketing](https://mlsanity.com/paper/2306.00315/) (2023)
- [Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization](https://mlsanity.com/paper/2206.01022/) (2022)
- [Intelligent Request Strategy Design in Recommender System](https://mlsanity.com/paper/2206.12296/) (2022)
- [End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling](https://mlsanity.com/paper/2408.11623/) (2024)
- [Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees](https://mlsanity.com/paper/2312.15566/) (2023)
- [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)
- [Multi-Treatment Multi-Task Uplift Modeling for Enhancing User Growth](https://mlsanity.com/paper/2408.12803/) (2024)
- [Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance](https://mlsanity.com/paper/2201.12692/) (2022)
- [Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression](https://mlsanity.com/paper/2207.09139/) (2022)
- [Uplift modeling with continuous treatments: A predict-then-optimize approach](https://mlsanity.com/paper/2412.09232/) (2024)
- [Generalized Causal Tree for Uplift Modeling](https://mlsanity.com/paper/2202.02416/) (2022)
- [GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace](https://mlsanity.com/paper/2203.10975/) (2022)
- [Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment](https://mlsanity.com/paper/2211.07264/) (2022)
- [Uplift vs. predictive modeling: a theoretical analysis](https://mlsanity.com/paper/2309.12036/) (2023)
- [Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing](https://mlsanity.com/paper/2502.15697/) (2025)
- [A New Transformation Approach for Uplift Modeling with Binary Outcome](https://mlsanity.com/paper/2310.05549/) (2023)
- [BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect](https://mlsanity.com/paper/2211.10793/) (2022)
- [Distribution-consistency Structural Causal Models](https://mlsanity.com/paper/2401.15911/) (2024)
- [MMCE: A Framework for Deep Monotonic Modeling of Multiple Causal Effects](https://mlsanity.com/paper/2504.03753/) (2025)
- [Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness](https://mlsanity.com/paper/2603.20775/) (2026)
- [Machine Learning Prescriptive Canvas for Optimizing Business Outcomes](https://mlsanity.com/paper/2206.10333/) (2022)
- [Causal Inference Based Single-branch Ensemble Trees For Uplift Modeling](https://mlsanity.com/paper/2302.01563/) (2023)
- [KDSM: An uplift modeling framework based on knowledge distillation and sample matching](https://mlsanity.com/paper/2303.02980/) (2023)
- [Answering Causal Queries at Layer 3 with DiscoSCMs-Embracing Heterogeneity](https://mlsanity.com/paper/2309.09323/) (2023)
- [Fairness Evaluation for Uplift Modeling in the Absence of Ground Truth](https://mlsanity.com/paper/2403.12069/) (2024)
- [Distilling interpretable causal trees from causal forests](https://mlsanity.com/paper/2408.01023/) (2024)
- [Are causal effect estimations enough for optimal recommendations under multitreatment scenarios?](https://mlsanity.com/paper/2410.05177/) (2024)
- [Which Company Adjustment Matter? Insights from Uplift Modeling on Financial Health](https://mlsanity.com/paper/2506.19049/) (2025)
- [Consistent Labeling Across Group Assignments: Variance Reduction in Conditional Average Treatment Effect Estimation](https://mlsanity.com/paper/2507.04332/) (2025)

## Abstract (arXiv)

A central question in many fields of scientific research is to determine how an outcome would be affected by an action, or to measure the effect of an action (a.k.a treatment effect). In recent years, a need for estimating the heterogeneous treatment effects conditioning on the different characteristics of individuals has emerged from research fields such as personalized healthcare, social science, and online marketing. To meet the need, researchers and practitioners from different communities have developed algorithms by taking the treatment effect heterogeneity modeling approach and the uplift modeling approach, respectively. In this paper, we provide a unified survey of these two seemingly disconnected yet closely related approaches under the potential outcome framework. We then provide a structured survey of existing methods by emphasizing on their inherent connections with a set of unified notations to make comparisons of the different methods easy. We then review the main applications of the surveyed methods in personalized marketing, personalized medicine, and social studies. Finally, we summarize the existing software packages and present discussions based on the use of methods on synthetic, semi-synthetic and real world data sets and provide some general guidelines for choosing methods.