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 Explain Key fit Authors explain ey of causal inference in modern terminologies of machine learning and I found it much readable than others They also cover a wide spectrum of ongoing approaches and issues in the field and make insightful connections between them Since the book covers so many topics however most topics "are only sketchily touched and technical proofs are mostly left out Moreover authors concentrate mostly on theoretical issues ex identifi. "only sketchily touched and technical proofs are mostly left out Moreover authors concentrate mostly on theoretical issues ex identifi. multivariate cases The authors consider analyzing statistical #asymmetries between cause and effect to be highly instructive and they #between cause and effect to be highly instructive and they on their decade of intensive research into this problemThe book is accessible to readers with a background in machine learning or statistics and can be used in graduate courses or as a reference for researchers The text includes code snippets that can be copied and pasted exercises and an appendix with a summary of the most important technical concepts. Good More like a giant survey paper
than a textbook but honestly that s what I wantUpdate 10072020 it s not an ideal textbook a textbook but honestly that s what I wantUpdate 10072020 it s not an
ideal textbook causality but it is far and away the best book on causality I ve found textbook causality but it is far and away the best book on causality I ve found 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 ac After reading The Book of Why I was looking for a technical introduction to #from them or their ac After reading The Book of Why I was looking for a technical introduction to Since by background in machine learning using ernel methods this book co authored by Bernhard Sch lkopf seemed a 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 explaining the need for causal models and discussing some of the principles underlying causal inference the book teaches. ,
Ood startThough I skimmed through the latter chapters the beginning gives a good introduction to the different types of causality and which assumptions that have to be made I especially #Liked The Chapters Drawing Links #the chapters drawing links causality and topics like transfer learning and domain adaptation This book provides a nice 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. 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 be exploited 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 beThe bivariate case turns out to be particularly hard
PROBLEM FOR CAUSAL LEARNING BECAUSE THERE ARE NO CONDITIONALfor causal learning because there are no conditional as used by classical methods for sol.