(Pdf/E–book) [Elements of Causal Inference] author Jonas Peters
This book provides a nice introduction into today causal inference research For A Person Like a person like who is vaguely interested in the topic 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 et an overview of today s rapid diverse research on the topic this book is a perfect fit Authors explain key ideas 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 top. 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. .
Jonas Peters ¿ 4 Free downloadT book on causality
I ve found Unlike Pearl it ives a reasonably rigorous treatment of the field and the authors are still uite activeve found Unlike Pearl it ives a reasonably rigorous treatment of the field and the authors are still uite active 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 #for a technical introduction to causality Since by background in machine learning using kernel methods this book authored by Bernhard Sch lkopf seemed a ood startThough I skimmed through the latter chapters this book co authored by Bernhard Sch lkopf seemed a ood startThough I skimmed through the latter chapters beginning The Lives of Stay-at-Home Fathers: Masculinity, Carework and Fatherhood in the United States gives aood introduction to the different types of causality and which assumptions that have to be made I especially liked the chapters drawing links between causality and topics like transfer learning and domain adaptatio. 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 problemThe book is accessible to readers with a background in machine learning or statistics and can be used in raduate 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. .
Ics are only sketchily
touched and technical proofs are mostly left out Moreover authors concentrate mostly on theoretical issues ex identifiability and applicationsand technical proofs are mostly left out Moreover authors concentrate mostly on theoretical issues ex identifiability and applications real world problems are only occasionally out Moreover authors concentrate mostly on theoretical issues ex identifiability and applications to real world problems are only occasionally This book only serves as a starting point and you need to follow references to really understand any topic I expected deeper and entler dive at least for key concepts I also found latter half of the book to be not as #Carefully Written As In The #written as in the so many parentheses and hyphens which are uite distracting Good More like a iant survey paper than a textbook but honestly that s what I wantUpdate 10072020 it s not an ideal textbook on causality but it is far and away the bes. 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 eneral 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.