Partial Identification of Probability Distributions

Charles F. Manski is Board of Trustees Professor at Northwestern University. He is author of Identification Problems in the Social Sciences and Analog Estimation Methods in Econometrics. He is a Fellow of the American Academy of Arts and Sciences, the American Association for the Advancement of Science, and the Econometric Society.

From the reviews: "Charles Manski has produced a nice and compact text written with extreme care, providing technical detail, and mathematical proofs where needed." Biometrics, March 2005 "This book is an excellent and rigorous presentation of the state of research in the area of partial identification of populations and credible inference, in which the author has made many important contributions. ... The overall quality of the book is very good. ... The main part of each chapter is written in a textbook style. ... Clearly, both methodology and the applications presented are intended to provide statisticians with a good foundation for further study in the subject ... ." (Evdokia Xekalaki, Zentralblatt MATH, Vol. 1047 (22), 2004) "I found the material very pertinent, departing, as it does, from the usual parametric approach in which the conclusions depend rather critically on the probability model adopted. Given a chance, it will make the traditionalist, like me, stop and think and perhaps, try to mend their ways a little. The main part of each chapter is written in textbook style, but fairly formally and rigorously ... . At the end of each chapter appear 'Complements', giving examples and extensions, and 'Endnotes'... ." (M. J. Crowder, Short Book Reviews, Vol. 23 (3), 2003) "This book, containing ten chapters, is the first comprehensive presentation of the theory of partial identification of probability distributions. It gives an overview of the research into this topic." (M. Riedel, Mathematical Reviews, Issue 2006 g) "The book is carefully and thoughtfully written. Some chapters start with a cogent section on the "anatomy of the problem," and all end with complements addressing specific contexts." (Alan F. Kaar, Journal of the American Statistical Association, Vol. 102, No. 477, 2007)

• Introduction: Partial Identification and Credible Inference.- Missing Outcomes.- Instrumental Variables.- Conditional Prediction with Missing Data.- Contaminated Outcomes.- Regressions, Short and Long.- Response-Based Sampling.- Analysis of Treatment Response.- Mnotone Treatment Response.- Monotone Instrumental Variables.- The Mixing Problem.