Many of the concepts and terminology surrounding modern causal inference can be quite intimidating to the novice. Judea Pearl presents a book ideal for beginners in statistics, providing a comprehensive introduction to the field of causality. Examples from classical statistics are presented throughout to demonstrate the need for causality in resolving decision-making dilemmas posed by data. Causal methods are also compared to traditional statistical methods, whilst questions are provided at the end of each section to aid student learning.
Judea Pearl, Computer Science and Statistics, University of California, Los Angeles, USA
Madelyn Glymour, Philosophy, Carnegie Mellon University, Pittsburgh, USA
Nicholas P. Jewell, Biostatistics and Statistics, University of California, Berkeley, USA