Ausality and which assumptions that have to *Be Made I Especially Liked The Chapters *made I especially liked the chapters links between causality and topics like transfer learning and domain adaptation This book rovides a nice introduction into today s causal inference research For a erson 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 erfect fit Authors 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 models from observational and interventional data how causal ideas could be exploited for classical machine learning Once Upon a Seduction (Its All About Attitude 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 aarticularly hard roblem for causal learning because there are no conditional independences as problem for causal learning because there are no conditional independences as by classical methods for sol. ,
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Unlike Pearl it gives a reasonably rigorous treatment of the field and the authors are still uite active inPearl it gives a reasonably rigorous treatment of the field and the authors are still uite active in half the apers 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 "in machine learning using kernel methods this book co authored by 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 explaining the need for causal models and discussing some of the rinciples underlying causal inference the book teaches. Also cover a wide spectrum of ongoing Approaches And Issues In The Field And and issues in the field and insightful connections between them Since the book covers so many topics however most topics are only sketchily touched and technical roofs are mostly left out Moreover authors concentrate mostly on theoretical issues ex identifiability and applications to real world roblems are only occasionally discussed This book only serves as a
Starting Point And Youpoint and you to follow references to really understand any topic I expected deeper and gentler dive at least for key concepts I also found latter half of the book to be not as carefully written as in the beginning so many arentheses and hyphens which are uite distractin. Ving multivariate cases The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive and they report on their decade of intensive research into this Until You Loved Me (Silver Springs, 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 andasted exercises and an appendix with a summary of the most important technical concepts. .