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Learning latent causal graphs via mixture oracles
Bohdan Kivva · Goutham Rajendran · Pradeep Ravikumar · Bryon Aragam

Fri Dec 10 08:30 AM -- 10:00 AM (PST) @

We study the problem of reconstructing a causal graphical model from data in the presence of latent variables. The main problem of interest is recovering the causal structure over the latent variables while allowing for general, potentially nonlinear dependencies. In many practical problems, the dependence between raw observations (e.g. pixels in an image) is much less relevant than the dependence between certain high-level, latent features (e.g. concepts or objects), and this is the setting of interest. We provide conditions under which both the latent representations and the underlying latent causal model are identifiable by a reduction to a mixture oracle. These results highlight an intriguing connection between the well-studied problem of learning the order of a mixture model and the problem of learning the bipartite structure between observables and unobservables. The proof is constructive, and leads to several algorithms for explicitly reconstructing the full graphical model. We discuss efficient algorithms and provide experiments illustrating the algorithms in practice.

Author Information

Bohdan Kivva (University of Chicago)
Goutham Rajendran (University of Chicago)

I obtained my CS PhD from UChicago. Recently, I've been actively working on causal representation learning and generative models. Some of my recent side projects were on NeRF (Computer vision) and Automatic Speech Recognition. I also have extensive competitive programming experience and a track publication record.

Pradeep Ravikumar (Carnegie Mellon University)
Bryon Aragam (University of Chicago)

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