research-article

Instrumental Variable-Driven Domain Generalization with Unobserved Confounders

Authors:

Kun KuangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 8

Article No.: 118, Pages 1 - 21

https://doi.org/10.1145/3595380

Published: 28 June 2023 Publication History

Abstract

Domain generalization (DG) aims to learn from multiple source domains a model that can generalize well on unseen target domains. Existing DG methods mainly learn the representations with invariant marginal distribution of the input features, however, the invariance of the conditional distribution of the labels given the input features is more essential for unknown domain prediction. Meanwhile, the existing of unobserved confounders which affect the input features and labels simultaneously cause spurious correlation and hinder the learning of the invariant relationship contained in the conditional distribution. Interestingly, with a causal view on the data generating process, we find that the input features of one domain are valid instrumental variables for other domains. Inspired by this finding, we propose an instrumental variable-driven DG method (IV-DG) by removing the bias of the unobserved confounders with two-stage learning. In the first stage, it learns the conditional distribution of the input features of one domain given input features of another domain. In the second stage, it estimates the relationship by predicting labels with the learned conditional distribution. Theoretical analyses and simulation experiments show that it accurately captures the invariant relationship. Extensive experiments on real-world datasets demonstrate that IV-DG method yields state-of-the-art results.

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Cited By

Miao QYuan JZhang SWu FKuang K(2024)Domaindiff: Boost out-of-Distribution Generalization with Synthetic DataICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10446788(5640-5644)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10446788
Zhu DLi YYuan JLi ZKuang KWu C(2023)Universal Domain Adaptation via Compressive Attention Matching2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.00642(6951-6962)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICCV51070.2023.00642

Index Terms

Instrumental Variable-Driven Domain Generalization with Unobserved Confounders
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Causal reasoning and diagnostics
  2. Machine learning
    1. Machine learning approaches
      1. Logical and relational learning
        Statistical relational learning

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 17, Issue 8

September 2023

348 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3596449

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

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Publication History

Published: 28 June 2023

Online AM: 29 April 2023

Accepted: 20 April 2023

Revised: 14 March 2023

Received: 25 July 2022

Published in TKDD Volume 17, Issue 8

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National Key Research and Development
National Natural Science Foundation of China
Young Elite Scientists Sponsorship Program by CAST
Zhejiang Provincial Natural Science Foundation of China
Major Technological Innovation Project of Hangzhou
Zhejiang Province Natural Science Foundation
Project by Shanghai AI Laboratory
Program of Zhejiang Province Science and Technology
StarryNight Science Fund of Zhejiang University Shanghai Institute for Advanced Study
Fundamental Research Funds for the Central Universities

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Cited By

Miao QYuan JZhang SWu FKuang K(2024)Domaindiff: Boost out-of-Distribution Generalization with Synthetic DataICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10446788(5640-5644)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10446788
Zhu DLi YYuan JLi ZKuang KWu C(2023)Universal Domain Adaptation via Compressive Attention Matching2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.00642(6951-6962)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICCV51070.2023.00642

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