论文标题
因果机器学习：调查和开放问题
Causal Machine Learning: A Survey and Open Problems
论文作者
论文摘要
因果机器学习（Causalml）是机器学习方法的伞术语，将数据生成过程正式化为结构性因果模型（SCM）。这种观点使我们能够理解此过程变化的影响（干预措施）以及事后发生的事情（反事实）会发生什么。我们根据他们解决的问题将Causalml中的工作分为五组：（1）因果监督学习，（2）因果生成建模，（3）因果解释，（4）因果公平性，以及（5）因果关系。我们系统地比较每个类别中的方法，并指出开放问题。此外，我们回顾了计算机视觉，自然语言处理和图形表示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个新生领域状态的批判性讨论，包括对未来工作的建议。
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.
