Learning Latent Structural Causal Models

• URL: http://arxiv.org/abs/2210.13583v1
• Date: Mon, 24 Oct 2022 20:09:44 GMT
• Title: Learning Latent Structural Causal Models
• Authors: Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth, Nan Rosemary Ke, Tristan Deleu, Stefan Bauer, Derek Nowrouzezahrai, Samira Ebrahimi Kahou
• Abstract summary: In machine learning tasks, one often operates on low-level data like image pixels or high-dimensional vectors. We present a tractable approximate inference method which performs joint inference over the causal variables, structure and parameters of the latent Structural Causal Model.
• Score: 31.686049664958457
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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