Counterfactual Analysis and Causal Machine Learning

Standard predictive models can identify correlations, but answering what happens when you take a specific action requires causal thinking. This text-based guide teaches you how to move beyond prediction to measure direct impact and reason through counterfactual scenarios. You will gain a practical understanding of how interventions affect target outcomes across business and data science contexts. What you'll learn: - Understand the core concepts of causal inference, confounding variables, and counterfactual reasoning - Differentiate correlation from causation using structured causal graphs and potential outcome frameworks - Apply modern estimation techniques to measure treatment effects in observational data - Implement causal machine learning methods for personalized decision-making and uplift modeling - Evaluate model assumptions and conduct sensitivity analysis to ensure robust conclusions The course begins by clarifying essential causal terminology and foundational statistical principles before guiding you through practical modeling strategies and code-based scenarios. Designed for data practitioners, analysts, and beginners in causal inference, no prior experience in causal modeling is required. Start reading to master causal decision-making with machine learning.