Deep Structural Causal Models for Tractable Counterfactual Inference

06/11/2020 ∙ by Nick Pawlowski, et al. ∙ 48

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



There are no comments yet.


page 7

page 8

page 13

page 15

page 17

page 18

page 19

page 21

Code Repositories


Repository for Deep Structural Causal Models for Tractable Counterfactual Inference

view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.