Identification for Prediction and Decision
This book is a full-scale exposition of Charles Manski's new methodology for analyzing empirical questions in the social sciences. He recommends that researchers first ask what can be learned from data alone, and then ask what can be learned when data are combined with credible weak assumptions. Inferences predicated on weak assumptions, he argues, can achieve wide consensus, while ones that require strong assumptions almost inevitably are subject to sharp disagreements.
Building on the foundation laid in the author's "Identification Problems in the Social Sciences" (Harvard, 1995), the book's fifteen chapters are organized in three parts. Part I studies prediction with missing or otherwise incomplete data. Part II concerns the analysis of treatment response, which aims to predict outcomes when alternative treatment rules are applied to a population. Part III studies prediction of choice behavior.
Each chapter juxtaposes developments of methodology with empirical or numerical illustrations. The book employs a simple notation and mathematical apparatus, using only basic elements of probability theory.
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In this revised-monograph-turned-textbook, Manski exposits his method of going "back to the basics" and learning what the data can tell us without assumption. Especially valuable is the emphasis on the difference between identification problems and statistical problems. Good reading for any graduate student or professional wishing to explore the nature of research in the social sciences.
Prediction with Incomplete Data
Decomposition of Mixtures
The Mixing Problem
Planning under Ambiguity
Planning with Sample Data
Predicting Choice Behavior
Studying Human Decision Processes