This article explores how causal inference can contribute to modern machine learning research by addressing key challenges in transfer learning, generalization, and robustness. It argues that causal representation learning—discovering high-level causal variables from low-level observations—can help overcome the limitations of current machine learning methods. The paper reviews fundamental concepts of causal inference and their implications for machine learning, emphasizing the need for models that can handle distribution shifts, interventions, and counterfactual reasoning. It discusses the differences between statistical and causal models, highlighting the importance of causal graphical models and structural causal models (SCMs) in capturing the underlying mechanisms of data generation. The article also introduces the independent causal mechanism (ICM) principle, which posits that causal variables are independent and do not influence each other, and the sparse mechanism shift (SMS) hypothesis, which suggests that small distribution changes typically affect only a subset of causal factors. The paper further explores the challenges of causal discovery from data, emphasizing the need for assumptions on function classes to enable learning from finite data sets. It concludes by discussing the potential of combining causal reasoning with machine learning to develop more robust and generalizable models.This article explores how causal inference can contribute to modern machine learning research by addressing key challenges in transfer learning, generalization, and robustness. It argues that causal representation learning—discovering high-level causal variables from low-level observations—can help overcome the limitations of current machine learning methods. The paper reviews fundamental concepts of causal inference and their implications for machine learning, emphasizing the need for models that can handle distribution shifts, interventions, and counterfactual reasoning. It discusses the differences between statistical and causal models, highlighting the importance of causal graphical models and structural causal models (SCMs) in capturing the underlying mechanisms of data generation. The article also introduces the independent causal mechanism (ICM) principle, which posits that causal variables are independent and do not influence each other, and the sparse mechanism shift (SMS) hypothesis, which suggests that small distribution changes typically affect only a subset of causal factors. The paper further explores the challenges of causal discovery from data, emphasizing the need for assumptions on function classes to enable learning from finite data sets. It concludes by discussing the potential of combining causal reasoning with machine learning to develop more robust and generalizable models.