報(bào)告題目：Using VAE for Causal Inference in Observational Data
報(bào)告人：Jiuyong Li 南澳大學(xué)/阿德萊德大學(xué) 教授
時間：2024年12月17號上午10點(diǎn)
地點(diǎn)：翡翠科教樓A座706
報(bào)告摘要：Treatment effect estimation is a crucial task in various fields, including medical research, public health, economics, and commerce. While causal effect estimation is typically performed through randomized trial experiments, the high costs and ethical constraints associated with such experiments make treatment effect estimation using observational data highly desirable. One of the key challenges in estimating treatment effects from observational data is dealing with confounders, especially the presence of unobserved confounders. In many applications, only proxies of the underlying confounders, or partial confounders, are observable. Variational Autoencoders (VAEs) are an effective representation learning technique for inferring the latent representation of such unobserved variables using their proxies. This talk will discuss recent research on estimating treatment effects in observational data using VAEs to model proxies of confounders or partial confounders.
主講人簡介：Jiuyong Li is a professor at the University of South Australia. He received his PhD in computer science from Griffith University, Australia, in 2002. His research interests include data mining, machine learning, causal discovery and inference, and bioinformatics. He has published more than 200 papers in leading journals and conferences in his field. His research has been extensively supported by prestigious Australian Research Council Discovery grants, as well as many industry and Cooperative Research Centre grants. He has received four visiting research professor fellowships from prestigious organizations, including the Australian Academy of Science and the Japan Society for the Promotion of Science. He has served as both Conference Chair and Program Chair of the Australasian Joint Conference on Artificial Intelligence.
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