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Weighted Euclidean balancing for a matrix exposure in estimating causal effect

  • Juan Chen und Yingchun Zhou ORCID logo EMAIL logo
Veröffentlicht/Copyright: 23. Mai 2025
The International Journal of Biostatistics
Aus der Zeitschrift The International Journal of Biostatistics Band 21 Heft 1

Abstract

With the increasing complexity of data, researchers in various fields have become increasingly interested in estimating the causal effect of a matrix exposure, which involves complex multivariate treatments, on an outcome. Balancing covariates for the matrix exposure is essential to achieve this goal. While exact balancing and approximate balancing methods have been proposed for multiple balancing constraints, dealing with a matrix treatment introduces a large number of constraints, making it challenging to achieve exact balance or select suitable threshold parameters for approximate balancing methods. To address this challenge, the weighted Euclidean balancing method is proposed, which offers an approximate balance of covariates from an overall perspective. In this study, both parametric and nonparametric methods for estimating the causal effect of a matrix treatment is proposed, along with providing theoretical properties of the two estimations. To validate the effectiveness of our approach, extensive simulation results demonstrate that the proposed method outperforms alternative approaches across various scenarios. Finally, we apply the method to analyze the causal impact of the omics variables on the drug sensitivity of Vandetanib. The results indicate that EGFR CNV has a significant positive causal effect on Vandetanib efficacy, whereas EGFR methylation exerts a significant negative causal effect.

Keywords: causal inference; matrix treatment; weighting methods; overall imbalance; observational study
Corresponding author: Yingchun Zhou, KLATASDS-MOE, School of Statistics, East China Normal University, 3663 North Zhongshan Road, Shanghai, 200062, P.R. China; and Institute of Brain and Education Innovation, East China Normal University, Shanghai, P.R. China, E-mail: 

Funding source: Shanghai “Science and technology Innovation Action Plan” Computational Biology Key Project

Award Identifier / Grant number: 23JS1400500

Award Identifier / Grant number: 23JS1400800

Funding source: Natural Science Foundation of Shanghai Municipality

Award Identifier / Grant number: 24ZR1420400

  1. Research ethics: The local Institutional Review Board deemed the study exempt from review.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: We used ChatGPT to improvethe language of the manuscript.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: The research was supported by Natural Science Foundation of Shanghai Municipality (No. 24ZR1420400) and Shanghai “Science and technology Innovation Action Plan” Computational Biology Key Project (No. 23JS1400500, No. 23JS1400800).

  7. Data availability: The data can be obtained upon request from the corresponding author.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/ijb-2024-0021).

Received: 2024-02-26
Accepted: 2025-04-07
Published Online: 2025-05-23

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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  12. Short Communication
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  14. Research Articles
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https://doi.org/10.1515/ijb-2024-0021
Schlagwörter für diesen Artikel
causal inference; matrix treatment; weighting methods; overall imbalance; observational study

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. Prognostic adjustment with efficient estimators to unbiasedly leverage historical data in randomized trials
  4. Homogeneity test and sample size of response rates for AC 1 in a stratified evaluation design
  5. A review of survival stacking: a method to cast survival regression analysis as a classification problem
  6. DsubCox: a fast subsampling algorithm for Cox model with distributed and massive survival data
  7. A hybrid hazard-based model using two-piece distributions
  8. Regression analysis of clustered current status data with informative cluster size under a transformed survival model
  9. Bayesian covariance regression in functional data analysis with applications to functional brain imaging
  10. Risk estimation and boundary detection in Bayesian disease mapping
  11. An improved estimator of the logarithmic odds ratio for small sample sizes using a Bayesian approach
  12. Short Communication
  13. A multivariate Bayesian learning approach for improved detection of doping in athletes using urinary steroid profiles
  14. Research Articles
  15. Guidance on individualized treatment rule estimation in high dimensions
  16. Weighted Euclidean balancing for a matrix exposure in estimating causal effect
  17. Penalized regression splines in Mixture Density Networks
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