%0 Journal Article %1 pawlowski2020structural %A Pawlowski, Nick %A Castro, Daniel C. %A Glocker, Ben %D 2020 %K causal-analysis neurips2020 readings %T Deep Structural Causal Models for Tractable Counterfactual Inference %U http://arxiv.org/abs/2006.06485 %X 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.

@article{pawlowski2020structural, 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.}, added-at = {2020-09-28T00:50:40.000+0200}, author = {Pawlowski, Nick and Castro, Daniel C. and Glocker, Ben}, biburl = {https://www.bibsonomy.org/bibtex/253ce714a66071a881de2880e9645e0a9/kirk86}, description = {[2006.06485] Deep Structural Causal Models for Tractable Counterfactual Inference}, interhash = {14f9039c923b636bb831eda1564ed39a}, intrahash = {53ce714a66071a881de2880e9645e0a9}, keywords = {causal-analysis neurips2020 readings}, note = {cite arxiv:2006.06485}, timestamp = {2020-09-28T00:50:40.000+0200}, title = {Deep Structural Causal Models for Tractable Counterfactual Inference}, url = {http://arxiv.org/abs/2006.06485}, year = 2020 }