Deep Structural Causal Models for Tractable Counterfactual Inference

• URL: http://arxiv.org/abs/2006.06485v2
• Date: Thu, 22 Oct 2020 21:59:47 GMT
• Title: Deep Structural Causal Models for Tractable Counterfactual Inference
• Authors: Nick Pawlowski, Daniel C. Castro, Ben Glocker
• Abstract summary: We formulate a general framework for building structural causal models (SCMs) with deep learning components. Our framework is validated on a synthetic dataset built on MNIST as well as on a real-world medical dataset of brain MRI scans.
• Score: 24.26709730032233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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