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Poster
High Fidelity Image Counterfactuals with Probabilistic Causal Models
Fabio De Sousa Ribeiro · Tian Xia · Miguel Monteiro · Nick Pawlowski · Ben Glocker
We present a general causal generative modelling framework for accurate estimation of high fidelity image counterfactuals with deep structural causal models. Estimation of interventional and counterfactual queries for high-dimensional structured variables, such as images, remains a challenging task. We leverage ideas from causal mediation analysis and advances in generative modelling to design new deep causal mechanisms for structured variables in causal models. Our experiments demonstrate that our proposed mechanisms are capable of accurate abduction and estimation of direct, indirect and total effects as measured by axiomatic soundness of counterfactuals.
Author Information
Fabio De Sousa Ribeiro (Imperial College London)
Tian Xia (Imperial College London)
Miguel Monteiro (Imperial College London)
Nick Pawlowski (Imperial College London)
Ben Glocker (Imperial College London)
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