Anna Seigal (Harvard University)
Causal disentanglement is the problem of finding a representation of data involving variables that relate causally to one another. In this talk, the focus will be on linear causal disentanglement: variables that relate via a linear causal model involving latent and observed variables. I will describe work to find sufficient and, in the worst case, necessary conditions for identifiability of the linear causal disentanglement setup. This is based on joint work with Chandler Squires, Salil Bhate, and Caroline Uhler.
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