Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction

by Guido Imbens, Donald B. Rubin

Most questions in social and biomedical sciences are causal in what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including, matching, propensity score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.

Genres: Textbooks, Mathematics, Economics, Reference, Nonfiction

644 pages, Hardcover

First published October 31, 2014

Reviews

[Tri · Rating 5 out of 5]

The best book on the potential outcome framework. I am not a big fan of propensity score so didn't go into chapters on this topic. But for RCT and IV, I have read more than three times, really fantastic!

[LvPinPin · Rating 5 out of 5]

Great, but too long and a little tedious

[Noor]

i want to get some idea from the social statistics

[William · Rating 5 out of 5]

Absolute unit

[Onur Bal · Rating 5 out of 5]

Standard defining.