research-article

Estimating Individualized Causal Effect with Confounded Instruments

Authors:

Kun KuangAuthors Info & Claims

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 1857 - 1867

https://doi.org/10.1145/3534678.3539335

Published: 14 August 2022 Publication History

Abstract

Learning individualized causal effect (ICE) plays a vital role in various fields of big data analysis, ranging from fine-grained policy evaluation to personalized treatment development. However, the presence of unmeasured confounders increases the difficulty of estimating ICE in real-world scenarios. A wide range of methods have been proposed to address the unmeasured confounders with the aid of instrument variable (IV), which sources from the treatment randomization. The performance of these methods relies on the well-predefined IVs that satisfy the unconfounded instruments assumption (i.e., the IVs are independent with the unmeasured confounders given observed covariates), which is untestable and leads to finding a valid IV becomes an art rather than science. In this paper, we focus on estimating the ICE with confounded instruments that violate the unconfounded instruments assumption. By considering the conditional independence between the set of confounded instruments and the outcome variable, we propose a novel method, named CVAE-IV, to generate a substitute of the unmeasured confounder with a conditional variational autoencoder. Our theoretical analysis guarantees that the generated confounder substitute will identify unbiased ICE. Extensive experiments on bias demand prediction and Mendelian randomization analysis verify the effectiveness of our method.

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In this paper, we concentrate on estimating individualized causal effect with invalid IVs that violate the unconfounded assumption. By constructing the CVAE-IV model to generate a ignorable confounder substitute, we isolate the influence of the unmeasured confounder from the estimation on ICE. Experiments on airline demand simulation and Mendelian randomization analysis verify the validity of our method with nearly unbiased estimation results of ICE.

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Cheng DXu ZLi JLiu LLiu JGao WLe TWooldridge MDy JNatarajan S(2024)Instrumental variable estimation for causal inference in longitudinal data with time-dependent latent confoundersProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i10.29029(11480-11488)Online publication date: 20-Feb-2024
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Index Terms

Estimating Individualized Causal Effect with Confounded Instruments
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Causal reasoning and diagnostics
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by regression
2. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic representations
      1. Causal networks

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cover image ACM Conferences

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2022

5033 pages

ISBN:9781450393850

DOI:10.1145/3534678

General Chairs:
Aidong Zhang
University of Virginia
,
Huzefa Rangwala
Amazon/George Mason University

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Published: 14 August 2022

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National Natural Science Foundation of China
Young Elite Scientists Sponsorship Program by CAST
the Fundamental Research Funds for the Central Universities
Key Laboratory for Corneal Diseases Research of Zhejiang Province, Project by Shanghai AI Laboratory

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KDD '22

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KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2022

Washington DC, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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https://doi.org/10.3390/e27020196
Li HZheng CWang SWu KWang HWu PGeng ZChen XZhou XSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Relaxing the accurate imputation assumption in doubly robust learning for debiased collaborative filteringProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693255(29448-29460)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693255
Cheng DXu ZLi JLiu LLiu JGao WLe TWooldridge MDy JNatarajan S(2024)Instrumental variable estimation for causal inference in longitudinal data with time-dependent latent confoundersProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i10.29029(11480-11488)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i10.29029
Wang HWang ZNiu YLiu ZLi HLiao YHuang YLiu X(2024)An Accurate and Interpretable Framework for Trustworthy Process MonitoringIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.33196065:5(2241-2252)Online publication date: May-2024
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Wang JLi HZhang CLiang DYu EOu WWang W(2023)CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00174(1355-1360)Online publication date: 1-Dec-2023
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