This book builds a basic knowledge framework for causal inference. It mainly introduces the basic theories, models, methods, and cutting-edge applications of intelligent causal inference through five chapters: Rubin causal model, Pearl causal model, causal effect calculation based on graphical models, causal structure learning, and causal machine learning, leading students into the interesting and challenging field of causal inference.
Reader comments
This book is very practical! For a courier like me who travels around the city, it's like a guide that helps me better understand cause and effect. The content in the book is very detailed and explained clearly, making it easy for a layman like me to understand. I particularly enjoyed the examples, which allowed me to connect theory with practical applications. However, I kind of hate those complicated mathematical formulas, they seem a bit of a headache. This reminds me of the book "Big Data Era", they both explore the importance of data and causality. I think this book is also very suitable for people who are interested in data analysis.
This book is very practical! For a courier like me who travels around the city, it's like a guide that helps me better understand cause and effect. The content in the book is very detailed and explained clearly, making it easy for a layman like me to understand. I particularly enjoyed the examples, which allowed me to connect theory with practical applications. However, I kind of hate those complicated mathematical formulas, they seem a bit of a headache. This reminds me of the book "Big Data Era", they both explore the importance of data and causality. I think this book is also very suitable for people who are interested in data analysis.