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 allows one to reason about the effects of changes to this process (i.e., interventions) and what would have happened in hindsight (i.e., counterfactuals). We categorize work in \causalml into five groups according to the problems they tackle: (1) causal supervised learning, (2) causal generative modeling, (3) causal explanations, (4) causal fairness, (5) causal reinforcement learning. For each category, we systematically compare its methods and point out open problems. Further, we review 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.
翻譯：Causal Machinening(Causal Machinening)是機械學習方法的總括術語,將數據生成過程正規化為結構性因果模型(SCM),使人們可以解釋這一過程變化的影響(即幹預)和事後觀察(即反事實)中會發生什麼。我們根據所處理的問題,將\causmal的工作分為五組:(1)因果監督學習,(2)因果分類模型,(3)因果解釋,(4)因果公正,(5)因果強化學習。我們係統地比較其方法,指出未解決的問題。此外,我們審查計算機願景、自然語言處理和圖表表述學習中特定模式的應用。最後,我們概述了因果基準,並批判性地討論了這個新生領域的狀況,包括為未來工作提出的建議。