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- W4307415369 abstract "Causal learning has long concerned itself with the accurate recovery of underlying causal mechanisms. Such causal modelling enables better explanations of out-of-distribution data. Prior works on causal learning assume that the high-level causal variables are given. However, in machine learning tasks, one often operates on low-level data like image pixels or high-dimensional vectors. In such settings, the entire Structural Causal Model (SCM) -- structure, parameters, textit{and} high-level causal variables -- is unobserved and needs to be learnt from low-level data. We treat this problem as Bayesian inference of the latent SCM, given low-level data. For linear Gaussian additive noise SCMs, we present a tractable approximate inference method which performs joint inference over the causal variables, structure and parameters of the latent SCM from random, known interventions. Experiments are performed on synthetic datasets and a causally generated image dataset to demonstrate the efficacy of our approach. We also perform image generation from unseen interventions, thereby verifying out of distribution generalization for the proposed causal model." @default.
- W4307415369 created "2022-11-01" @default.
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- W4307415369 date "2022-10-24" @default.
- W4307415369 modified "2023-09-23" @default.
- W4307415369 title "Learning Latent Structural Causal Models" @default.
- W4307415369 doi "https://doi.org/10.48550/arxiv.2210.13583" @default.
- W4307415369 hasPublicationYear "2022" @default.
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