Poster
in
Workshop: Spurious correlations, Invariance, and Stability (SCIS)
Latent Variable Models for Bayesian Causal Discovery
Jithendaraa Subramanian · Jithendaraa Subramanian · Yashas Annadani · Ivaxi Sheth · Stefan Bauer · Derek Nowrouzezahrai · Samira Ebrahimi Kahou
Keywords: [ Causal Representation Learning ] [ Bayesian Inference ] [ Latent Variable models ]
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.