Identification for Prediction and Decision

Juxtaposing methodology with empirical and numerical illustrations, this book is a full-scale exposition of a new approach for analyzing empirical questions in the social sciences. Manski recommends that researchers first ask what can be learned from data alone, and then what can be learned when data are combined with credible weak assumptions.

Table of Contents:
Cover Title Page Copyright Dedication Contents Preface The Reflection Problem The Law of Decreasing Credibility Identification and Statistical Inference Coping with Ambiguity Organization of the Book The Developing Literature on Partial Identification 1.1 Predicting Criminality 1.2 Probabilistic Prediction 1.3 Estimation of Best Predictors from Random Samples& 1.4 Extrapolation 1.5 Predicting High School Graduation Complement 1A. Best Predictors under Square and Absolute Loss& Complement 1B. Nonparametric Regression Analysis Complement 1C. Word Problems 2. Missing Outcomes 2.1 Anatomy of the Problem 2.2 Bounding the Probability of Exiting Homelessness 2.3 Means of Functions of the Outcome 2.4 Parameters That Respect Stochastic Dominance 2.5 Distributional Assumptions 2.6 Wage Regressions and the Reservation-Wage Model of Labor Supply 2.7 Statistical Inference Complement 2A. Interval Measurement of Outcomes Complement 2B. Jointly Missing Outcomes and Covariates Complement 2C. Convergence of Sets to Sets 3.1 Distributional Assumptions and Credible Inference&# 3.2 Missingness at Random 3.3 Statistical Independence 3.4 Equality of Means 3.5 Inequality of Means Complement 3A. Imputations and Nonresponse Weights& Complement 3B. Conditioning on the Propensity Score Complement 3C. Word Problems 4.1 The Normal-Linear Model of Market and Reservation Wages 4.2 Selection Models 4.3 Parametric Models for Best Predictors Complement 4A. Minimum-Distance Estimation of Partially Identified Models& 5.1 The Inferential Problem and Some Manifestations 5.2 Binary Mixing Covariates 5.3 Contamination through Imputation 5.4 Instrumental Variables Complement 5A. Sharp Bounds on Parameters That Respect Stochastic Dominance 6. Response-Based Sampling 6.1 The Odds Ratio and Public Health 6.2 Bounds on Relative and Attributable Risk 6.3 Information on Marginal Distributions 6.4 Sampling from One Response Stratum 6.5 General Binary Stratifications II. Analysis of Treatment Response 7. The Selection Problem 7.1 Anatomy of the Problem 7.2 Sentencing and Recidivism 7.3 Randomized Experiments 7.4 Compliance with Treatment Assignment 7.5 Treatment by Choice 7.6 Treatment at Random in Nonexperimental Settings 7.7 Homogeneous Linear Response Complement 7A. Perspectives on Treatment Comparison Complement 7B. Word Problems 8.1 Simultaneity in Competitive Markets 8.2 The Linear Market Model 8.3 Equilibrium in Games 8.4 The Reflection Problem 9.1 Shape Restrictions 9.2 Bounds on Parameters That Respect Stochastic Dominance 9.3 Bounds on Treatment Effects 9.4 Monotone Response and Selection 9.5 Bounding the Returns to Schooling 10.1 Extrapolation from Experiments to Rules with Treatment Variation& 10.2 Extrapolation from the Perry Preschool Experiment& 10.3 Identification of Event Probabilities with the Experimental Evidence Alone 10.4 Treatment Response Assumptions 10.5 Treatment Rule Assumptions 10.6 Combining Assumptions 11.1 Studying Treatment Response to Inform Treatment Choice 11.2 Criteria for Choice under Ambiguity 11.3 Treatment Using Data from an Experiment with Partial Compliance&# 11.4 An Additive Planning Problem 11.5 Planning with Partial Knowledge of Treatment Response 11.6 Planning and the Selection Problem 11.7 The Ethics of Fractional Treatment Rules 11.8 Decentralized Treatment Choice Complement 11A. Minimax-Regret Rules for Two Treatments Are Fractional Complement 11B. Reporting Observable Variation in Treatment Response&# Complement 11C. Word Problems 12.1 Statistical Induction 12.2 Wald’sDevelopment of Statistical Decision Theory&# 12.3 Using a Randomized Experiment to Evaluate an Innovation&# III. Predicting Choice Behavior 13. Revealed Preference Analysis 13.1 Revealing the Preferences of an Individual 13.2 Random Utility Models of Population Choice Behavior&# 13.3 College Choice in America 13.4 Random Expected-Utility Models Complement 13A. Prediction Assuming Strict Preferences Complement 13B. Axiomatic Decision Theory 14. Measuring Expectations 14.1 Elicitation of Expectations from Survey Respondents&# 14.2 Illustrative Findings 14.3 Using Expectations Data to Predict Choice Behavior 14.4 Measuring Ambiguity Complement 14A. The Predictive Power of Intentions Data: A Best-Case Analysis& Complement 14B. Measuring Expectations of Facts 15. Studying Human Decision Processes 15.1 As-If Rationality and Bounded Rationality& 15.2 Choice Experiments 15.3 Prospects for a Neuroscientific Synthesis& References Author Index Subject Index

About the Author :
Charles F. Manski is Board of Trustees Professor of Economics at Northwestern University.

Review :
More than anyone else, Charles Manski has changed the way we think about identification. This book contains the most comprehensive discussion of his work in this area. It is a must-read for everybody interested in identification, and there isn't an empirical economist or econometrician who can afford not to be. -- Guido Imbens, Harvard University Charles Manski is a highly original and influential voice in econometrics. His work on partial identification and nonparametric bounds now holds a central position in many areas of theoretical and applied research. This comprehensive yet accessible text brings together the author's research on incomplete data, on treatment response and on choice behavior. It is an important contribution to our knowledge and will stand as a key reference for students and researchers for years to come. -- Richard Blundell, University College London