In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed.
In 2015, Kearney and Levine sought to evaluate the longterm impacts of the program in a retrospective evaluation carried out in the United States. Taking advantage of limitations in television broadcasting technology in the early years ...
Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference.
This book offers a self-contained and concise introduction to causal models and how to learn them from data.
Alternative graphical causal models and the identification of direct effects. In: P. Shrout, editor. Causality and Psychopathology: Finding the Determinants of Disorders and Their Cures. Oxford University Press.
The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence .
These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical ...
These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest.
Datasets, R code, and solutions to odd-numbered exercises are available on the book's website at www.routledge.com/9780367705053. Instructors can also find slides based on the book, and a full solutions manual under 'Instructor Resources'.
This book is about making machine learning models and their decisions interpretable.
This book offers a new model of educational achievement to explain why some students are committed to preparation for college. On the Edge of Commitment is a provocative assessment of how young people decide how far to go in school.