survey

A Unified Survey of Treatment Effect Heterogeneity Modelling and Uplift Modelling

Authors:

Lin LiuAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 8

Article No.: 162, Pages 1 - 36

https://doi.org/10.1145/3466818

Published: 04 October 2021 Publication History

Abstract

A central question in many fields of scientific research is to determine how an outcome is affected by an action, i.e., to estimate the causal effect or treatment effect of an action. In recent years, in areas such as personalised healthcare, sociology, and online marketing, a need has emerged to estimate heterogeneous treatment effects with respect to individuals of different characteristics. To meet this need, two major approaches have been taken: treatment effect heterogeneity modelling and uplifting modelling. Researchers and practitioners in different communities have developed algorithms based on these approaches to estimate the heterogeneous treatment effects. In this article, we present a unified view of these two seemingly disconnected yet closely related approaches under the potential outcome framework. We provide a structured survey of existing methods following either of the two approaches, emphasising their inherent connections and using unified notation to facilitate comparisons. We also review the main applications of the surveyed methods in personalised marketing, personalised medicine, and sociology. Finally, we summarise and discuss the available software packages and source codes in terms of their coverage of different methods and applicability to different datasets, and we provide general guidelines for method selection.

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Cited By

Kirpichenko SUtkin LKonstantinov AMuliukha V(2024)BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment EffectAlgorithms10.3390/a1701004017:1(40)Online publication date: 18-Jan-2024
https://doi.org/10.3390/a17010040
Li KZhu L(2024)A New Transformation Approach for Uplift Modeling with Binary OutcomeProceedings of the 2024 9th International Conference on Mathematics and Artificial Intelligence10.1145/3670085.3670089(73-77)Online publication date: 10-May-2024
https://dl.acm.org/doi/10.1145/3670085.3670089
Sun ZChen X(2024) M 3 TN: Multi-Gate Mixture-of-Experts Based Multi-Valued Treatment Network for Uplift Modeling ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10446323(5065-5069)Online publication date: 14-Apr-2024
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Index Terms

A Unified Survey of Treatment Effect Heterogeneity Modelling and Uplift Modelling
1. Applied computing
2. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Causal reasoning and diagnostics
  2. Machine learning

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 8

November 2022

754 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3481697

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 04 October 2021

Accepted: 01 May 2021

Revised: 01 May 2021

Received: 01 August 2020

Published in CSUR Volume 54, Issue 8

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