Learning Latent Structural Causal Models

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 task...

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Bibliographic Details
Main Authors Subramanian, Jithendaraa, Annadani, Yashas, Sheth, Ivaxi, Ke, Nan Rosemary, Deleu, Tristan, Bauer, Stefan, Nowrouzezahrai, Derek, Kahou, Samira Ebrahimi
Format Journal Article
LanguageEnglish
Published 24.10.2022
Subjects
Computer Science - Artificial Intelligence
Computer Science - Learning
Statistics - Methodology
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Summary: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.
DOI:10.48550/arxiv.2210.13583