Identification for prediction and decision / Charles F. Manski.

Format:

Book

Author/Creator:

Manski, Charles F., author.

Language:

English

Subjects (All):

Social sciences--Methodology.

Social sciences.

Social prediction.

Decision making.

Physical Description:

1 online resource (365 pages)

Edition:

1st ed.

Place of Publication:

Cambridge, Massachusetts : Harvard University Press, 2007.

Summary:

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 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.

Contents:

Cover

Title Page

Copyright

Dedication

Contents

Preface

Introduction

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

I. Prediction with Incomplete Data

1. Conditional Prediction

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. Instrumental Variables

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. Parametric Prediction

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. Decomposition of Mixtures.

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. Linear Simultaneous Equations

8.1 Simultaneity in Competitive Markets

8.2 The Linear Market Model

8.3 Equilibrium in Games

8.4 The Reflection Problem

9. Monotone Treatment Response

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. The Mixing Problem

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. Planning under Ambiguity

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. Planning with Sample Data

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.

Notes:

Includes bibliographical references and indexes.

Description based on print version record.

Description based on online resource ; title from PDF title page (Ebook Central, viewed December 5, 2025).

Description based on publisher supplied metadata and other sources.

ISBN:

9780674033665

OCLC:

1281972751