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.

翻译：Causal Machinening(Causal Machinening)是机械学习方法的总括术语,将数据生成过程正规化为结构性因果模型(SCM),这一视角使我们得以了解该过程变化(干预)的影响以及事后观察(对抗事实)中会发生的情况。我们将CausalML的工作按照它们处理的问题分为五组:(1)因果监督学习,(2)因果分类模型,(3)因果解释,(4)因果公正,(5)因果强化学习。我们系统地比较每一类方法,指出未解决的问题。此外,我们审查了计算机视觉、自然语言处理和图表表述学习中的数据-模式特定应用。最后,我们概述了因果基准和对这一新生领域状况的批判性讨论,包括未来工作的建议。