Deep Structural Causal Models for Tractable Counterfactual Inference

Nick Pawlowski; Daniel C. Castro; Ben Glocker

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arXiv · 2006.06485

Deep Structural Causal Models for Tractable Counterfactual Inference

Nick Pawlowski·Daniel C. Castro·Ben Glocker

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Abstract

We formulate a general framework for building structural causal models (SCMs) with deep learning components. The proposed approach employs normalising flows and variational inference to enable tractable inference of exogenous noise variables - a crucial step for counterfactual inference that is missing from existing deep causal learning methods. Our framework is validated on a synthetic dataset built on MNIST as well as on a real-world medical dataset of brain MRI scans. Our experimental results indicate that we can successfully train deep SCMs that are capable of all three levels of Pearl's ladder of causation: association, intervention, and counterfactuals, giving rise to a powerful new approach for answering causal questions in imaging applications and beyond. The code for all our experiments is available at https://github.com/biomedia-mira/deepscm.

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BibTeX RIS

Nick Pawlowski, Daniel C. Castro, Ben Glocker. 2020-06-11. Deep Structural Causal Models for Tractable Counterfactual Inference. https://arxiv.org/abs/2006.06485

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