(Elements of Causal Inference) [PDF/EBOOK] º Jonas Peters
Ausality and which assumptions that have to be made I specially liked
The Chapters Drawing Links chapters drawing links causality
and topics like transfer learning and domain adaptation This book provides topics like transfer learning and domain adaptation This book provides liked the chapters drawing links between causality and topics like transfer learning and domain adaptation This book provides a introduction into today s causal inference research For a person like me who is vaguely interested in the topic but 1 find classical writings like Pearl s to be difficult to understand because they are not written in the language of modern statistics machine learning and 2 want to get an overview of today s rapid diverse research on the topic this book is a perfect fit Authors A Certain Justice (Adam Dalgliesh, explain key ideas of causal inference in modern terminologies of machine learning and I found it much readable than others They. Readers how to use causal models how to compute intervention distributions how to infer causal models from observational and interventional data and how causal ideas could bexploited for classical machine learning problems All of these topics are discussed first in terms of two variables and then in the general multivariate case The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for sol. Good More like a giant survey
"PAPER THAN A TEXTBOOK BUT HONESTLY "than a textbook but honestly s what I wantUpdate 10072020 it s not an ideal textbook on causality but it is far honestly that s what I wantUpdate 10072020 it s not an ideal textbook on causality but it is far away the best book on causality I ve found Unlike Pearl it gives a reasonably rigorous treatment of the field and the authors are still uite active in causality half the papers I read are from them or their academic children After reading The Book of Why I was looking for a technical introduction to causality Since by background in machine learning using kernel methods this book co authored by Bernhard Sch lkopf seemed a good startThough I skimmed through the latter chapters the beginning gives a good introduction to the different types of A concise and self contained introduction to causal inference increasingly important in data science and machine learningThe mathematization of causality is a relatively recent development and has become increasingly important in data science and machine learning This book offers a self contained and concise introduction to causal models and how to learn them from data After xplaining the need for causal models and discussing some of the principles underlying causal inference the book teaches.