Identification for Prediction and Decision / / Charles F. Manski.

• Superior document: Title is part of eBook package: De Gruyter HUP eBook Package Archive 1893-1999
• VerfasserIn: Manski, Charles F.,
• Place / Publishing House: Cambridge, MA : : Harvard University Press, , [2009]; ©2008
• Year of Publication: 2009
• Language: English
• Physical Description: 1 online resource (368 p.)

This book provides a language and a set of tools for finding bounds on the predictions that social and behavioral scientists can logically make from nonexperimental and experimental data. The economist Charles F. Manski draws on examples from criminology, demography, epidemiology, social psychology, and sociology as well as economics to illustrate this language and to demonstrate the broad usefulness of the tools. There are many traditional ways to present identification problems in econometrics, sociology, and psychometrics. Some of these are primarily statistical in nature, using concepts such as flat likelihood functions and nondistinct parameter estimates. Manski’s strategy is to divorce identification from purely statistical concepts and to present the logic of identification analysis in ways that are accessible to a wide audience in the social and behavioral sciences. In each case, problems are motivated by real examples with real policy importance, the mathematics is kept to a minimum, and the deductions on identifiability are derived giving fresh insights. Manski begins with the conceptual problem of extrapolating predictions from one population to some new population or to the future. He then analyzes in depth the fundamental selection problem that arises whenever a scientist tries to predict the effects of treatments on outcomes. He carefully specifies assumptions and develops his nonparametric methods of bounding predictions. Manski shows how these tools should be used to investigate common problems such as predicting the effect of family structure on children’s outcomes and the effect of policing on crime rates. Successive chapters deal with topics ranging from the use of experiments to evaluate social programs, to the use of case-control sampling by epidemiologists studying the association of risk factors and disease, to the use of intentions data by demographers seeking to predict future fertility. The book closes by examining two central identification problems in the analysis of social interactions: the classical simultaneity problem of econometrics and the reflection problem faced in analyses of neighborhood and contextual effects.

Table of Contents

Frontmatter -- Contents -- Preface -- Introduction -- I. Prediction with Incomplete Data -- 1. Conditional Prediction -- 2.Missing Outcomes -- 3. Instrumental Variables -- 4. Parametric Prediction -- 5. Decomposition of Mixtures -- 6. Response-Based Sampling -- II. Analysis of Treatment Response -- 7. The Selection Problem -- 8. Linear Simultaneous Equations -- 9. Monotone Treatment Response -- 10. The Mixing Problem -- 11. Planning under Ambiguity -- 12. Planning with Sample Data -- III. Predicting Choice Behavior -- 13. Revealed Preference Analysis -- 14. Measuring Expectations -- 15. Studying Human Decision Processes -- References -- Author Index -- Subject Index