{"title":"Book Review of Explanation in Causal Inference: Methods of Mediation and Interaction (author: T.J. Vanderweele)","authors":"L. Keele","doi":"10.1353/obs.2016.0007","DOIUrl":null,"url":null,"abstract":"Explanation in Causal Inference: Methods of Mediation and Interaction is an introductory text on two widely used methods in statistical analysis: mediation and interaction. The book is both meant to serve as an introduction to these two topics, but also provides considerable mathematical detail in a lengthy appendix. Importantly, the treatment of these two topics is entirely grounded in a counterfactual framework. The counterfactual framework, often referred to as the potential outcomes framework, has been hailed as a revolution in how we think about causality and statistical analysis. I would agree with that sentiment, but the impact of the counterfactual framework is varied. On some topics, the insights have been less revolutionary, but in other areas this framework has I think completely revised how we think. The topics of mediation and interaction analysis are two that I would say have been seriously changed by the counterfactual framework. I think there is already a fairly widespread understanding of how mediation analysis has changed, and this book will only help further spread that awareness. On the topic of interaction analysis, I think there is less appreciation for how the counterfactual framework has changed thinking. This book serves as the remedy.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1353/obs.2016.0007","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Observational studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1353/obs.2016.0007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Explanation in Causal Inference: Methods of Mediation and Interaction is an introductory text on two widely used methods in statistical analysis: mediation and interaction. The book is both meant to serve as an introduction to these two topics, but also provides considerable mathematical detail in a lengthy appendix. Importantly, the treatment of these two topics is entirely grounded in a counterfactual framework. The counterfactual framework, often referred to as the potential outcomes framework, has been hailed as a revolution in how we think about causality and statistical analysis. I would agree with that sentiment, but the impact of the counterfactual framework is varied. On some topics, the insights have been less revolutionary, but in other areas this framework has I think completely revised how we think. The topics of mediation and interaction analysis are two that I would say have been seriously changed by the counterfactual framework. I think there is already a fairly widespread understanding of how mediation analysis has changed, and this book will only help further spread that awareness. On the topic of interaction analysis, I think there is less appreciation for how the counterfactual framework has changed thinking. This book serves as the remedy.