Research Post
This paper provides a generative approach for causal inference using data from observational studies. Inspired by the work of Kingma et al. (2014), we propose a sequence of three architectures (namely Series, Parallel, and Hybrid) that each incorporate their M1 and M2 models as building blocks. Each architecture is an improvement over the previous one in terms of estimating causal effect, culminating in the Hybrid model. The Hybrid model is designed to encourage decomposing the underlying factors of any observational dataset; this in turn, helps to accurately estimate all treatment outcomes. Our empirical results demonstrate the superiority of all three proposed architectures compared to both state-of-the-art discriminative as well as other generative approaches in the literature.
Feb 15th 2022
Research Post
Read this research paper, co-authored by Amii Fellow and Canada CIFAR AI Chair Osmar Zaiane: UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-Wise Perspective with Transformer
Jun 1st 2021
Research Post
May 1st 2021
Research Post
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