Book_CausalInference

Project information

This book presents a clear and accessible introduction to Causal Inference, one of the main conceptual pillars of modern data science. Aimed at students, researchers, and beginners, it explains how to identify, estimate, and interpret cause-and-effect relationships in data, emphasizing the essential distinction between correlation and causation for reliable, evidence-based decisions.

The text combines applied knowledge with recognized academic literature, offering a solid foundation to understand how causal inference supports analytical reasoning in statistics, economics, social sciences, and machine learning. The focus is on key principles and simple examples that make causal reasoning intuitive, even for readers without advanced technical background.

data science