Product Description
Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a selection of Donald B. Rubin's research articles on matched sampling, from the early 1970s, when the author was one of the major researchers involved in establishing the field, to recent contributions to this now extremely active area. The articles include fundamental theoretical studies that have become classics, important extensions, and real applications that range from breast cancer treatments to tobacco litigation to studies of criminal tendencies. They are organized into seven parts, each with an introduction by the author that provides historical and personal context and discusses the relevance of the work today. A concluding essay offers advice to investigators designing observational studies. The book provides an accessible introduction to the study of matched sampling and will be an indispensable reference for students and researchers in statistics, epidemiology, medicine, economics, education, sociology, political science, and anyone else doing empirical research to evaluate the causal effects of interventions.

CUSTOMER REVIEWS (Average Customer Rating: 4.5 based on 2 reviews)

Great book!

This is a very good collection of important articles that Rubin and his students and colleagues have written on causal inference.
Another book written by his student P. Rosenbaum (now a Wharton prof) titled "Observtional Studies" is also very important if one wants to learn the details of propensity scoring or matching.
November 20, 2007

causal inference by one of the originators

An important issue for researchers is the discovery of cause and effect relationships. It is often the case that those not well educated in statistics will think that a simple correlation between two variables is enough to imply causation (perhaps because of temporal order i.e. A comes befor B so A causes B). However, determining causation is a much more complicated issue. A common statistical adage is "correlation does not imply causation". Don Rubin is an accomplished author, teacher and one of the leading developers of the statistical theory of causation. the authorities to read on this subject are Don Rubin and Judea Pearl, two of the pioneers who have written texts on this topics. This theory involves new concepts that one does not learn in introductory statistics course. Contrafactuals represent one such concept. Read this book to learn the details.
August 13, 2007

SIMILAR PRODUCTS

Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research)
by Stephen L. Morgan, Christopher Winship

Data Analysis Using Regression and Multilevel/Hierarchical Models
by Andrew Gelman, Jennifer Hill

Observational Studies
by Paul R. Rosenbaum

Principles of Statistical Inference
by D. R. Cox

Identification for Prediction and Decision
by Charles F. Manski