Causal Inference in Machine Learning

This chapter explores the critical differences between traditional machine learning and causal machine learning, emphasizing the latter's role in enhancing decision-making rather than just predictions. It discusses the importance of causal relationships and 'what if' questions in business contexts, showcasing how effective leaders use these concepts to drive innovation. Additionally, the chapter addresses the practical challenges of implementing causal ML, highlighting the need for proper framing, experimentation, and a strong understanding of econometrics for impactful analysis.