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Latent Variable Models for Bayesian Causal Discovery. (arXiv:2207.05723v2 [cs.LG] UPDATED)
Aug. 12, 2022, 1:11 a.m. | Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth, Stefan Bauer, Derek Nowrouzezahrai, Samira Ebrahimi Kahou
stat.ML updates on arXiv.org arxiv.org
Learning predictors that do not rely on spurious correlations involves
building causal representations. However, learning such a representation is
very challenging. We, therefore, formulate the problem of learning a causal
representation from high dimensional data and study causal recovery with
synthetic data. This work introduces a latent variable decoder model, Decoder
BCD, for Bayesian causal discovery and performs experiments in mildly
supervised and unsupervised settings. We present a series of synthetic
experiments to characterize important factors for causal discovery and …
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