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Data Inference in Observational Settings

Data Inference in Observational Settings

Four Volume Set
Edited by:

December 2013 | 1 648 pages | SAGE Publications Ltd
Most social research is carried out in observational settings; that is, most social researchers collect information in the "real world" trying to do as little possible to alter the circumstances of study. However, there is a fundamental problem with this kind of research, in that it is very hard to draw "causal" conclusions, because of the complexity and obduracy of social reality. This is not just a problem for social scientists interested in policy or social action. It applies across the board more generally because it becomes difficult to know, without the conditions for credible inference, what conclusions can be drawn from any piece of empirical research that aspires to be anything more than descriptive of social phenomena.

This four-volume set of readings introduces the reader to the advances that have been made in trying to help social researchers draw more credible inferences from investigations carried out in observational settings. Drawing from a variety of sources - from logicians and philosophers, to applied statisticians, computer scientists and econometricians, to epidemiologists and social researchers - this collection provides an invaluable resource for scholars in the field.

Volume One: Background

Volume Two: Analytical Techniques

Volume Three: Temporal Relations

Volume Four: Experimental Analogues

Donald Rubin
Estimating Causal Effects of Treatments in Randomized and Non-Randomized Studies
Paul Holland
Statistics and Causal Inference
Kosuke Imai et al
Misunderstandings between Experimentalists and Observationalists about Causal Inference
Christoper Winship and Stephen Morgan
The Estimation of Causal Effects from Observational Data
Markus Gangl
Causal Inferences in Sociological Research
Jerry Splawa-Neyman, D. Dabrowski and T. Speed
On the Application of Probability Theory to Agricultural Experiments
Essay on Principles: Section Nine

Donald Rubin
Causal Inference Using Potential Outcomes
Design, Modeling, Decisions

James Fearon
Counterfactuals and Hypothesis-Testing in Political Science
Stephen Morgan
Counterfactuals, Causal Effect Heterogeneity and the Catholic School Effect on Learning
Robert Sampson et al
Does Marriage Reduce Crime? A Counterfactual Approach to within-Individual Causal Effects
Donald Campbell
Reforms as Experiments
Robert LaLonde
Evaluating the Econometric Evaluations of Training Programs with Experimental Data
James Heckman and V. Joseph Hotz
Choosing among Alternative Non-Experimental Methods for Estimating the Impact of Social Programs
The Case of Manpower Training

Jennifer Ahern et al
Estimating the Effects of Potential Public Health Interventions on Population Disease Burden
A Step-by-Step Illustration of Causal Inference Methods

Joshua Angrist and Jörn-Steffen Pischke
The Credibility Revolution in Empirical Economics
How Better Research Design Is Taking the Con out of Econometrics

W. Cochran
The Effectiveness of Adjustment by Subclassification in Removing Bias in Observational Studies
Rubin Rosenbaum
Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
Herbert Smith
Matching with Multiple Controls to Estimate Treatment Effects in Observational Studies
Stephen Morgan and David Harding
Matching Estimators of Causal Effects
Prospects and Pitfalls in Theory and Practice

Elizabeth Stuart
Matching Methods for Causal Inference
A Review and a Look forward

Paul Rosenbaum and Donald Rubin
The Central Role of the Propensity Score in Observational Studies for Causal Effects
Rajeev Dehejia and Sadek Wahba
Propensity Score-Matching Methods for Non-Experimental Causal Studies
Onur Baser
Too Much Ado about Propensity Score Models? Comparing Methods of Propensity Score Matching
Peter Austin et al
A Comparison of the Ability of Different Propensity Score Models to Balance Measured Variables between Treated and Untreated Subjects
A Monte Carlo Study

Matthias Schonlau et al
Selection Bias in Web Surveys and the Use of Propensity Scores
Sewall Wright
Correlation and Causation
Arthur Goldberger
Structural Equation Methods in the Social Sciences
Judea Pearl
Causal Diagrams for Empirical Research
Sonia Hernandez-Diaz et al
From Causal Diagrams to Birth Weight-Specific Curves of Infant Mortality
Geoffrey Wodtke et al
Neighborhood Effects in Temporal Perspective
The Impact of Long-Term Exposure to Concentrated Disadvantage on High School Graduation

David Heise
Causal Inference from Panel Data
Paul Allison
Panel Data to Estimate Effects of Events
Bruce Western
The Impact of Incarceration on Wage Mobility and Inequality
Charles Halaby
Panel Models in Sociological Research
Theory into Practice

Mauricio Avendano
Correlation or Causation? Income Inequality and Infant Mortality in Fixed Effects Models in the Period 1960-2008 in 34 OECD Countries
Zvi Griliches
Sibling Models and Data in Economics
Beginnings of a Survey

Robert Hauser and Peter Mossel
Fraternal Resemblance in Education Attainment and Occupational Status
Dalton Conley and Neil Bennett
Is Biology Destiny? Birth Weight and Life Chances
Inge Sieben and Paul de Graaf
Schooling or Social Origin? The Bias in the Effect of Educational Attainment on Social Orientations
Hans-Peter Kohler et al
Social Science Methods for Twins Data
Integrating Causality, Endowments and Heritability

John Bound et al
Problems with Instrumental Variables Estimation When the Correlation between the Instruments and the Endogeneous Explanatory Variable Is Weak
Joshua Angrist et al
Identification of Causal Effects Using Instrumental Variables
Daron Acemoglu et al
The Colonial Origins of Comparative Development
An Empirical Investigation

George Wheby et al
A Genetic Instrumental Variables Analysis of the Effects of Prenatal Smoking on Birth Weight
Evidence from Two Samples

Kenneth Bollen
Instrumental Variables in Sociology and the Social Sciences
Jay Kaufman et al
Causal Inference from Randomized Trials in Social Epidemiology
Michael Sobel
What Do Randomised Studies of Housing Mobility Demonstrate? Causal Inference in the Face of Interference
Thomas Cook et al
Three Conditions under Which Experiments and Observational Studies Produce Comparable Causal Estimates
New Findings from within-Study Comparisons

Guy Grossman and Delia Baldassarri
The Impact of Elections on Co-peration
Evidence from a Lab-in-the-Field Experiment in Uganda

Jens Ludwig et al
Neighborhood Effects on Long-Term Well-Being of Low-Income Adults
Donald Thistlethwaite and Donald Campbell
Regression-Discontinuity Analysis
An alternative to the ex post facto Experiment

Donald Rubin
Assignment to a Treatment Group on the Basis of a Covariate
Richard Berk and David Rauma
Capitalizing on Non-Random Assignment to Treatments
A Regression-Discontinuity Evaluation of a Crime-Control Program

Guido Imbens and Joshua Angrist
Identification and Estimation of Local Average Treatment Effects
Richard Berk and Jan de Leeuw
An Evaluation of California's Inmate Classification System Using a Generalized Regression Discontinuity Design
David Card and Alan Krueger
Minimum Wages and Employment
A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania

Bruce Meyer
Natural and Quasi-Experiments in Economics
Marianne Bertrand et al
How Much Should We Trust Differences-in-Differences Estimates?
David Kirk
A Natural Experiment on Residential Change and Recidivism
Lessons from Hurricane Katrina

Kate Strully et al
Effects of Prenatal Poverty on Infant Health
State-Earned Income Tax Credits and Birth Weight


While causal thinking is at the heart of social science research and explanation, too little rigorous attention is paid by researchers as how to strengthen claims of causality.  This comprehensive collection draws together some of the best papers that point to the challenges of establishing causality and provide ways of addressing many of these challenges. It provides the resources to help both researchers and students address the question of causality much more systematically and convincingly than is often the case.

Professor David de Vaus
Executive Dean, Faculty of Social and Behavioural Sciences, University of Queensland

An excellent collection of seminal papers summarizing the background to, and the state of the art for, methods which are becoming central to the conduct of epidemiology and other areas of health and social research in the 21st century.

Dr. Neil Pearce
Director, Centre for Global NCDs; Professor of Epidemiology and Biostatistics, London School of Hygiene and Tropical Medicine

These are the canonical papers on causal inference, organized for the first time into one useful handbook. It’s a must-have for all researchers in the social sciences. I shall be recommending it to all my students.

Ichiro Kawachi, M.D., Ph.D.
Professor of Social Epidemiology and Chair, Department of Social and Behavioral Sciences, Harvard School of Public Health

These volumes bring together a core set of important papers on the critical topic of causal inference and will prove to be an extremely useful source for recommended core reading for researchers and students alike. 

Professor Nick Wareham
Director MRC Epidemiology Unit and the UKCRC Centre for Diet and Physical Activity Public Health Research, University of Cambridge

This four-volume reader is the best place to start if you are interested in an overview of how to make cause inference from observational data. The selection concisely covers a vast literature that has rapidly developed over a period of several decades. You will read seminal methodological contributions, excellent review articles and important applications in these volumes. Instructors in the social sciences may use this reader for a graduate level methodology course. Researchers will find it a useful reference on their bookshelves. Policy analysts will enter a whole new world of dialogue if they become familiar with the rationale and techniques summarized in this reader.

Assistant Professor Jui-Chung Allen Li
Department of Social Research and Public Policy, NYU Abu Dhabi; and Institute of European and American Studies and Institute of Sociology, Academia Sinica (Taiwan)

For Chinese researchers and students, I believe a comprehensive collection of rigorous papers on causality will enhance the claims of study findings for a rapidly changing society. The handbook will provide a useful tool for researchers and students to meet the challenges of addressing causal relationships.

Professor Xiulan Zhang
Dean, School of Social Development and Public Policy, Director, China Institute of Health, Beijing Normal University; Vice-President, China Social Policy Association

In social science research, oftentimes, the researcher’s ultimate goal is to be able to make causal inference statements about what would contribute to socially significant outcomes. Unfortunately, not being able to implement true experimental design in most social science research situations makes such causal inference risky and full of pitfalls, as it can become very difficult to rule out rival hypotheses or explanations. This collection of seminal papers on issues related to making causal inferences provides a “must read” for social science researchers, green hand or experienced alike, who desire to avoid numerous pitfalls in the process of making causal inferences in social science research.

Xitao Fan, Ph.D.
Chair Professor & Dean, Faculty of Education, University of Macau, Macao, China

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ISBN: 9781446266502