  Causal Machine Learning (CausalML) is an umbrella term for machine learning
methods that formalize the data-generation process as a structural causal model
(SCM). This perspective enables us to reason about the effects of changes to
this process (interventions) and what would have happened in hindsight
(counterfactuals). We categorize work in CausalML into five groups according to
the problems they address: (1) causal supervised learning, (2) causal
generative modeling, (3) causal explanations, (4) causal fairness, and (5)
causal reinforcement learning. We systematically compare the methods in each
category and point out open problems. Further, we review data-modality-specific
applications in computer vision, natural language processing, and graph
representation learning. Finally, we provide an overview of causal benchmarks
and a critical discussion of the state of this nascent field, including
recommendations for future work.
