# Learning latent causal graphs via mixture oracles

@article{Kivva2021LearningLC, title={Learning latent causal graphs via mixture oracles}, author={Bohdan Kivva and Goutham Rajendran and Pradeep Ravikumar and Bryon Aragam}, journal={ArXiv}, year={2021}, volume={abs/2106.15563} }

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 dependence between the variables. 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), andβ¦Β Expand

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