宋心遠，博士生導師，香港中文大學教授，統計系系主任。獲“長白山學者講座教授”和教育部人才稱號。主要研究方向為生存分析、潛變量模型、貝葉斯方法及其在醫學、金融和社會學等領域內的應用。在具有國際影響力的頂級學術期刊發表SCI論文143 余篇，在Wiley出版重要英文學術專著1部，在6部英文專著中負責部分章節的編寫工作。獲批香港政府ECG(External Competitive Grant)-GRF(General Reach Fund)基金18項，國家自然科學基金面上項目3項。受邀參加國際會議(ICSA，COMPSTAT，IMS等)30余次，特別是2017年受德國心理測量學會年會(AMGPS)邀請，作為特邀報告人匯報潛變量在復雜數據上的應用。擔任多家統計學領域頂級學術期刊如Structural Equation Modeling: A Multidisciplinary Journal(IF:4.426)、Psychometrika(IF:2.743)、Biometrics(IF:1.755)等副主編。截止2021年1月份，Google學術引用近3000次，h-index=30。
報告摘要：Causal mediation analysis aims to investigate the underlying mechanism of how an exposure exerts its effects on the outcome mediated by intermediate variables. However, existing methods for causal mediation analysis in the context of survival models are primarily focused on estimating average causal effects and are difficult to apply to precision medicine. Recently, machine learning has emerged as a promising tool for precisely estimating individualized causal effects without assuming specific model forms. This study proposes a novel method, conditional generative adversarial network (CGAN)-based individualized causal mediation analysis with survival outcomes (CGAN-ICMA-SO), to infer individualized causal effects with survival outcomes based on the CGAN framework. We show that the estimated distribution of the proposed inferential conditional generator converges to the true conditional distribution under mild conditions. Our numerical experiments indicate that CGAN-ICMA-SO surpasses existing state-of-the-art methods. Applying the proposed method to an Alzheimer's disease (AD) Neuroimaging Initiative dataset reveals the individualized direct and indirect effects of the APOE4 allele on time to AD onset.
