Identification for Prediction and Decision

ISBN-10: 0674026535

ISBN-13: 9780674026537

Edition: 2007

Authors: Charles F. Manski

List price: $74.00
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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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Book details

List price: $74.00
Copyright year: 2007
Publisher: Harvard University Press
Publication date: 1/31/2008
Binding: Hardcover
Pages: 310
Size: 6.25" wide x 9.50" long x 1.00" tall
Weight: 1.408
Language: English

The Reflection Problem
The Law of Decreasing Credibility
Identification and Statistical Inference
Prediction and Decisions
Coping with Ambiguity
Organization of the Book
The Developing Literature on Partial Identification
Prediction with Incomplete Data
Conditional Prediction
Predicting Criminality
Probabilistic Prediction
Estimation of Best Predictors from Random Samples
Predicting High School Graduation
Best Predictors under Square and Absolute Loss
Nonparametric Regression Analysis
Word Problems
Missing Outcomes
Anatomy of the Problem
Bounding the Probability of Exiting Homelessness
Means of Functions of the Outcome
Parameters That Respect Stochastic Dominance
Distributional Assumptions
Wage Regressions and the Reservation-Wage Model of Labor Supply
Statistical Inference
Interval Measurement of Outcomes
Jointly Missing Outcomes and Covariates
Convergence of Sets to Sets
Instrumental Variables
Distributional Assumptions and Credible Inference
Missingness at Random
Statistical Independence
Equality of Means
Inequality of Means
Imputations and Nonresponse Weights
Conditioning on the Propensity Score
Word Problems
Parametric Prediction
The Normal-Linear Model of Market and Reservation Wages
Selection Models
Parametric Models for Best Predictors
Minimum-Distance Estimation of Partially Identified Models
Decomposition of Mixtures
The Inferential Problem and Some Manifestations
Binary Mixing Covariates
Contamination through Imputation
Instrumental Variables
Sharp Bounds on Parameters That Respect Stochastic Dominance
Response-Based Sampling
The Odds Ratio and Public Health
Bounds on Relative and Attributable Risk
Information on Marginal Distributions
Sampling from One Response Stratum
General Binary Stratifications
Analysis of Treatment Response
The Selection Problem
Anatomy of the Problem
Sentencing and Recidivism
Randomized Experiments
Compliance with Treatment Assignment
Treatment by Choice
Treatment at Random in Nonexperimental Settings
Homogeneous Linear Response
Perspectives on Treatment Comparison
Word Problems
Linear Simultaneous Equations
Simultaneity in Competitive Markets
The Linear Market Model
Equilibrium in Games
The Reflection Problem
Monotone Treatment Response
Shape Restrictions
Bounds on Parameters That Respect Stochastic Dominance
Bounds on Treatment Effects
Monotone Response and Selection
Bounding the Returns to Schooling
The Mixing Problem
Extrapolation from Experiments to Rules with Treatment Variation
Extrapolation from the Perry Preschool Experiment
Identification of Event Probabilities with the Experimental Evidence Alone
Treatment Response Assumptions
Treatment Rule Assumptions
Combining Assumptions
Planning under Ambiguity
Studying Treatment Response to Inform Treatment Choice
Criteria for Choice under Ambiguity
Treatment Using Data from an Experiment with Partial Compliance
An Additive Planning Problem
Planning with Partial Knowledge of Treatment Response
Planning and the Selection Problem
The Ethics of Fractional Treatment Rules
Decentralized Treatment Choice
Minimax-Regret Rules for Two Treatments Are Fractional
Reporting Observable Variation in Treatment Response
Word Problems
Planning with Sample Data
Statistical Induction
Wald's Development of Statistical Decision Theory
Using a Randomized Experiment to Evaluate an Innovation
Predicting Choice Behavior
Revealed Preference Analysis
Revealing the Preferences of an Individual
Random Utility Models of Population Choice Behavior
College Choice in America
Random Expected-Utility Models
Prediction Assuming Strict Preferences
Axiomatic Decision Theory
Measuring Expectations
Elicitation of Expectations from Survey Respondents
Illustrative Findings
Using Expectations Data to Predict Choice Behavior
Measuring Ambiguity
The Predictive Power of Intentions Data: A Best-Case Analysis
Measuring Expectations of Facts
Studying Human Decision Processes
As-If Rationality and Bounded Rationality
Choice Experiments
Prospects for a Neuroscientific Synthesis
Author Index
Subject Index
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