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Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data

Authors:

Stephen L. Rathbun,

Sheng LiAuthors Info & Claims

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Pages 176 - 184

https://doi.org/10.1145/3447548.3467302

Published: 14 August 2021 Publication History

Abstract

Treatment effect estimation from observational data is a critical research topic across many domains. The foremost challenge in treatment effect estimation is how to capture hidden confounders. Recently, the growing availability of networked observational data offers a new opportunity to deal with the issue of hidden confounders. Unlike networked data in traditional graph learning tasks, such as node classification and link detection, the networked data under the causal inference problem has its particularity, i.e., imbalanced network structure. In this paper, we propose a Graph Infomax Adversarial Learning (GIAL) model for treatment effect estimation, which makes full use of the network structure to capture more information by recognizing the imbalance in network structure. We evaluate the performance of our GIAL model on two benchmark datasets, and the results demonstrate superiority over the state-of-the-art methods.

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Treatment effect estimation from observational data is a critical research topic across many domains. The foremost challenge in treatment effect estimation is how to capture hidden confounders. Recently, the growing availability of networked observational data offers a new opportunity to deal with the issue of hidden confounders. Unlike networked data in traditional graph learning tasks, such as node classification and link detection, the networked data under the causal inference problem has its particularity, i.e., imbalanced network structure. In this paper, we propose a Graph Infomax Adversarial Learning (GIAL) model for treatment effect estimation, which makes full use of the network structure to capture more information by recognizing the imbalance in network structure. We evaluate the performance of our GIAL model on two benchmark datasets, and the results demonstrate superiority over the state-of-the-art methods.

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Cited By

Zhao ZBai YXiong RCao QMa CJiang NWu FKuang K(2024)Learning Individual Treatment Effects under Heterogeneous Interference in NetworksACM Transactions on Knowledge Discovery from Data10.1145/367376118:8(1-21)Online publication date: 16-Aug-2024
https://dl.acm.org/doi/10.1145/3673761
Sirazitdinov ABuchwald MHeuveline VHesser J(2024)Graph Neural Networks for Individual Treatment Effect EstimationIEEE Access10.1109/ACCESS.2024.343766512(106884-106894)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3437665
Panagopoulos GMalitesta DMalliaros FPang J(2024)Uplift Modeling Under Limited SupervisionMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70365-2_8(127-144)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70365-2_8
Show More Cited By

Index Terms

Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

August 2021

4259 pages

ISBN:9781450383325

DOI:10.1145/3447548

General Chairs:
Feida Zhu
Singapore Management University
,
Beng Chin Ooi
National University of Singapore
,
Chunyan Miao
Nanyang Technology University
,
Program Chairs:
Haixun Wang,
Iryna Skrypnyk,
Wynne Hsu,
Sanjay Chawla

Copyright © 2021 ACM.

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Published: 14 August 2021

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U.S. Army Research Office

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KDD '21

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KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2021

Virtual Event, Singapore

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Cited By

Zhao ZBai YXiong RCao QMa CJiang NWu FKuang K(2024)Learning Individual Treatment Effects under Heterogeneous Interference in NetworksACM Transactions on Knowledge Discovery from Data10.1145/367376118:8(1-21)Online publication date: 16-Aug-2024
https://dl.acm.org/doi/10.1145/3673761
Sirazitdinov ABuchwald MHeuveline VHesser J(2024)Graph Neural Networks for Individual Treatment Effect EstimationIEEE Access10.1109/ACCESS.2024.343766512(106884-106894)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3437665
Panagopoulos GMalitesta DMalliaros FPang J(2024)Uplift Modeling Under Limited SupervisionMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70365-2_8(127-144)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70365-2_8
Huang QMa JLi JGuo RSun HChang Y(2023)Modeling Interference for Individual Treatment Effect Estimation from Networked Observational DataACM Transactions on Knowledge Discovery from Data10.1145/362844918:3(1-21)Online publication date: 9-Dec-2023
https://dl.acm.org/doi/10.1145/3628449
Jiang SHuang ZLuo XSun YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical SystemsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599272(997-1009)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599272
Takeuchi KNishida RKashima HOnishi MSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Causal Effect Estimation on Hierarchical Spatial Graph DataProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599269(2145-2154)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599269
Wang YChu ZZhou TJiang CHao HZhu MCai XCui QLi LZhang JXue SZhou J(2023)Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00082(588-592)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00082
Chu ZLi RRathbun SLi S(2023)Continual Causal Inference with Incremental Observational Data2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00263(3430-3439)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00263
Lin XZhang GLu XBao HTakeuchi KKashima H(2023)Estimating Treatment Effects Under Heterogeneous InterferenceMachine Learning and Knowledge Discovery in Databases: Research Track10.1007/978-3-031-43412-9_34(576-592)Online publication date: 18-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-43412-9_34
Chu ZLi S(2023)Causal Effect Estimation: Recent Progress, Challenges, and OpportunitiesMachine Learning for Causal Inference10.1007/978-3-031-35051-1_5(79-100)Online publication date: 9-Aug-2023
https://doi.org/10.1007/978-3-031-35051-1_5
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