  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.
