research-article

Modeling Interference for Individual Treatment Effect Estimation from Networked Observational Data

Authors:

Yi ChangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 18, Issue 3

Article No.: 48, Pages 1 - 21

https://doi.org/10.1145/3628449

Published: 09 December 2023 Publication History

Abstract

Estimating individual treatment effect (ITE) from observational data has attracted great interest in recent years, which plays a crucial role in decision-making across many high-impact domains such as economics, medicine, and e-commerce. Most existing studies of ITE estimation assume that different units at play are independent and do not influence each other. However, many social science experiments have shown that there often exist different levels of interactions between units in observational data, especially in a networked environment. As a result, the treatment assignment of one unit can affect the outcome of other units connected to it in the network, which is referred to as the interference or spillover effect. In this article, we study an important problem of ITE estimation from networked observational data by modeling the interference between different units and provide a principled framework to support such study. Methodologically, we propose a novel framework, SPNet, that first captures the influence of hidden confounders with the aid of graph convolutional network and then models the interference by introducing an environment summary variable and developing a masked attention mechanism. Experimental evaluations on several semi-synthetic datasets based on real-world networks corroborate the superiority of our proposed framework over state-of-the-art individual treatment effect estimation methods.

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Cited By

Chou JChen JMarathe M(2024)State-Of-The-Art and Challenges in Causal Inference on Graphs: Confounders and Interferences2024 IEEE 6th International Conference on Cognitive Machine Intelligence (CogMI)10.1109/CogMI62246.2024.00011(1-13)Online publication date: 28-Oct-2024
https://doi.org/10.1109/CogMI62246.2024.00011
Chou JChen JMarathe M(2024)Estimate Causal Effects of Entangled Treatment on Graphs using Disentangled Instrumental Variables2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825713(1260-1267)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825713

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 18, Issue 3

April 2024

663 pages

EISSN:1556-472X

DOI:10.1145/3613567

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 December 2023

Online AM: 18 October 2023

Accepted: 26 September 2023

Revised: 29 March 2023

Received: 12 September 2022

Published in TKDD Volume 18, Issue 3

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Cited By

Chou JChen JMarathe M(2024)State-Of-The-Art and Challenges in Causal Inference on Graphs: Confounders and Interferences2024 IEEE 6th International Conference on Cognitive Machine Intelligence (CogMI)10.1109/CogMI62246.2024.00011(1-13)Online publication date: 28-Oct-2024
https://doi.org/10.1109/CogMI62246.2024.00011
Chou JChen JMarathe M(2024)Estimate Causal Effects of Entangled Treatment on Graphs using Disentangled Instrumental Variables2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825713(1260-1267)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825713

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