  Causal deep learning (CDL) is a new and important research area in the larger
field of machine learning. With CDL, researchers aim to structure and encode
causal knowledge in the extremely flexible representation space of deep
learning models. Doing so will lead to more informed, robust, and general
predictions and inference -- which is important! However, CDL is still in its
infancy. For example, it is not clear how we ought to compare different methods
as they are so different in their output, the way they encode causal knowledge,
or even how they represent this knowledge. This is a living paper that
categorises methods in causal deep learning beyond Pearl's ladder of causation.
We refine the rungs in Pearl's ladder, while also adding a separate dimension
that categorises the parametric assumptions of both input and representation,
arriving at the map of causal deep learning. Our map covers machine learning
disciplines such as supervised learning, reinforcement learning, generative
modelling and beyond. Our paradigm is a tool which helps researchers to: find
benchmarks, compare methods, and most importantly: identify research gaps. With
this work we aim to structure the avalanche of papers being published on causal
deep learning. While papers on the topic are being published daily, our map
remains fixed. We open-source our map for others to use as they see fit:
perhaps to offer guidance in a related works section, or to better highlight
the contribution of their paper.
