Causal mediation analysis with multiple mediators and censored outcomes by GAN approach

Li Zhanfeng
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Li Zhanfeng: Zhongnan University of Economics and Law

Chinese Stata Conference 2024 from Stata Users Group

Abstract: Mediation models with censored outcomes play a crucial role in social and medical sciences. However, the inherent censoring characteristics of the data often lead existing models to rely on assumptions of linearity, homogeneity, and normality for estimation. Unfortunately, these assumptions may not align with the complexities of real-world problems, limiting the persuasiveness of causal analyses. In this study, I investigate causal mediation analysis within a counterfactual framework by framing it as a neural style transfer problem commonly encountered in image processing. Acknowledging the impressive capabilities of generative adversarial networks (GANs) in handling neural style transfer, I propose a novel GAN-based model named generative adversarial censored mediation network to address mediation issues under my concern. My model employs recti

Date: 2024-10-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-dcm
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Persistent link: https://EconPapers.repec.org/RePEc:boc:chin24:08

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