Ricardo Silva
Causal inference is a well-established field in statistics, but it is still relatively underdeveloped within machine learning. This is partly due to the lack of good learning resources before Elements of Causal Inference came along. This book is high-quality work that breaks through, firmly establishing a connection between causal inference and general machine learning.
Endorsement
Causal inference is a well-established field in statistics, but it is still relatively underdeveloped within machine learning. This is partly due to the lack of good learning resources before Elements of Causal Inference came along. This book is high-quality work that breaks through, firmly establishing a connection between causal inference and general machine learning.
Ricardo Silva, Senior Lecturer, University College London; Turing Fellow, Alan Turing Institute
From the Publisher
Elements of Causal Inference is an important contribution to the growing literature on causal analysis. This lucid monograph elegantly weaves together statistics, machine learning, and causality to provide a holistic picture of how we and machines can use data to understand the world.
David Blei, Professor of Computer Science and Statistics, Columbia University
Causal inference is a well-established field in statistics, but it is still relatively underdeveloped within machine learning. This is partly due to the lack of good learning resources before Elements of Causal Inference came along. This book is high-quality work that breaks through, firmly establishing a connection between causal inference and general machine learning.
Ricardo Silva, Senior Lecturer, University College London; Turing Fellow, Alan Turing Institute
David Blei
Elements of Causal Inference is an important contribution to the growing literature on causal analysis. This lucid monograph elegantly weaves together statistics, machine learning, and causality to provide a holistic picture of how we and machines can use data to understand the world.