Conditional generative adversarial networks for individualized causal mediation analysis

Huan Cheng (), Sun Rongqian () and Song Xinyuan ()
Additional contact information
Huan Cheng: Department of Statistics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
Sun Rongqian: Department of Statistics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
Song Xinyuan: Department of Statistics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

Journal of Causal Inference, 2024, vol. 12, issue 1, 23

Abstract: Most classical methods popularly used in causal mediation analysis can only estimate the average causal effects and are difficult to apply to precision medicine. Although identifying heterogeneous causal effects has received some attention, the causal effects are explored using the assumptive parametric models with limited model flexibility and analytic power. Recently, machine learning is becoming a major tool for accurately estimating individualized causal effects, thanks to its flexibility in model forms and efficiency in capturing complex nonlinear relationships. In this article, we propose a novel method, conditional generative adversarial network (CGAN) for individualized causal mediation analysis (CGAN-ICMA), to infer individualized causal effects based on the CGAN framework. Simulation studies show that CGAN-ICMA outperforms five other state-of-the-art methods, including linear regression, k-nearest neighbor, support vector machine regression, decision tree, and random forest regression. The proposed model is then applied to a study on the Alzheimer’s disease neuroimaging initiative dataset. The application further demonstrates the utility of the proposed method in estimating the individualized causal effects of the apolipoprotein E-ε4 allele on cognitive impairment directly or through mediators.

Keywords: causal mediation analysis; CGAN; individualized causal effects (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jci-2022-0069 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:12:y:2024:i:1:p:23:n:1

DOI: 10.1515/jci-2022-0069

Access Statistics for this article

Journal of Causal Inference is currently edited by Elias Bareinboim, Jin Tian and Iván Díaz

More articles in Journal of Causal Inference from De Gruyter
Bibliographic data for series maintained by Peter Golla ().