Data Inference in Observational Settings

Data Inference in Observational Settings

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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
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Product details

  • Hardback | 1648 pages
  • 156 x 234 x 121.92mm | 3,100g
  • London, United Kingdom
  • English
  • Volume Set ed.
  • 1446266508
  • 9781446266502

Table of contents

Estimating Causal Effects of Treatments in Randomized and Non-Randomized Studies - Donald Rubin
Statistics and Causal Inference - Paul Holland
Misunderstandings between Experimentalists and Observationalists about Causal Inference - Kosuke Imai et al
The Estimation of Causal Effects from Observational Data - Christoper Winship and Stephen Morgan
Causal Inferences in Sociological Research - Markus Gangl
On the Application of Probability Theory to Agricultural Experiments - Jerry Splawa-Neyman, D. Dabrowski and T. Speed
Essay on Principles: Section Nine
Causal Inference Using Potential Outcomes - Donald Rubin
Design, Modeling, Decisions
Counterfactuals and Hypothesis-Testing in Political Science - James Fearon
Counterfactuals, Causal Effect Heterogeneity and the Catholic School Effect on Learning - Stephen Morgan
Does Marriage Reduce Crime? A Counterfactual Approach to within-Individual Causal Effects - Robert Sampson et al
Reforms as Experiments - Donald Campbell
Evaluating the Econometric Evaluations of Training Programs with Experimental Data - Robert LaLonde
Choosing among Alternative Non-Experimental Methods for Estimating the Impact of Social Programs - James Heckman and V. Joseph Hotz
The Case of Manpower Training
Estimating the Effects of Potential Public Health Interventions on Population Disease Burden - Jennifer Ahern et al
A Step-by-Step Illustration of Causal Inference Methods
The Credibility Revolution in Empirical Economics - Joshua Angrist and Joern-Steffen Pischke
How Better Research Design Is Taking the Con out of Econometrics
The Effectiveness of Adjustment by Subclassification in Removing Bias in Observational Studies - W. Cochran
Reducing Bias in Observational Studies Using Subclassification on the Propensity Score - Rubin Rosenbaum
Matching with Multiple Controls to Estimate Treatment Effects in Observational Studies - Herbert Smith
Matching Estimators of Causal Effects - Stephen Morgan and David Harding
Prospects and Pitfalls in Theory and Practice
Matching Methods for Causal Inference - Elizabeth Stuart
A Review and a Look forward
The Central Role of the Propensity Score in Observational Studies for Causal Effects - Paul Rosenbaum and Donald Rubin
Propensity Score-Matching Methods for Non-Experimental Causal Studies - Rajeev Dehejia and Sadek Wahba
Too Much Ado about Propensity Score Models? Comparing Methods of Propensity Score Matching - Onur Baser
A Comparison of the Ability of Different Propensity Score Models to Balance Measured Variables between Treated and Untreated Subjects - Peter Austin et al
A Monte Carlo Study
Selection Bias in Web Surveys and the Use of Propensity Scores - Matthias Schonlau et al
Correlation and Causation - Sewall Wright
Structural Equation Methods in the Social Sciences - Arthur Goldberger
Causal Diagrams for Empirical Research - Judea Pearl
From Causal Diagrams to Birth Weight-Specific Curves of Infant Mortality - Sonia Hernandez-Diaz et al
Neighborhood Effects in Temporal Perspective - Geoffrey Wodtke et al
The Impact of Long-Term Exposure to Concentrated Disadvantage on High School Graduation
Causal Inference from Panel Data - David Heise
Panel Data to Estimate Effects of Events - Paul Allison
The Impact of Incarceration on Wage Mobility and Inequality - Bruce Western
Panel Models in Sociological Research - Charles Halaby
Theory into Practice
Correlation or Causation? Income Inequality and Infant Mortality in Fixed Effects Models in the Period 1960-2008 in 34 OECD Countries - Mauricio Avendano
Sibling Models and Data in Economics - Zvi Griliches
Beginnings of a Survey
Fraternal Resemblance in Education Attainment and Occupational Status - Robert Hauser and Peter Mossel
Is Biology Destiny? Birth Weight and Life Chances - Dalton Conley and Neil Bennett
Schooling or Social Origin? The Bias in the Effect of Educational Attainment on Social Orientations - Inge Sieben and Paul de Graaf
Social Science Methods for Twins Data - Hans-Peter Kohler et al
Integrating Causality, Endowments and Heritability
Problems with Instrumental Variables Estimation When the Correlation between the Instruments and the Endogeneous Explanatory Variable Is Weak - John Bound et al
Identification of Causal Effects Using Instrumental Variables - Joshua Angrist et al
The Colonial Origins of Comparative Development - Daron Acemoglu et al
An Empirical Investigation
A Genetic Instrumental Variables Analysis of the Effects of Prenatal Smoking on Birth Weight - George Wheby et al
Evidence from Two Samples
Instrumental Variables in Sociology and the Social Sciences - Kenneth Bollen
Causal Inference from Randomized Trials in Social Epidemiology - Jay Kaufman et al
What Do Randomised Studies of Housing Mobility Demonstrate? Causal Inference in the Face of Interference - Michael Sobel
Three Conditions under Which Experiments and Observational Studies Produce Comparable Causal Estimates - Thomas Cook et al
New Findings from within-Study Comparisons
The Impact of Elections on Co-peration - Guy Grossman and Delia Baldassarri
Evidence from a Lab-in-the-Field Experiment in Uganda
Neighborhood Effects on Long-Term Well-Being of Low-Income Adults - Jens Ludwig et al
Regression-Discontinuity Analysis - Donald Thistlethwaite and Donald Campbell
An alternative to the ex post facto Experiment
Assignment to a Treatment Group on the Basis of a Covariate - Donald Rubin
Capitalizing on Non-Random Assignment to Treatments - Richard Berk and David Rauma
A Regression-Discontinuity Evaluation of a Crime-Control Program
Identification and Estimation of Local Average Treatment Effects - Guido Imbens and Joshua Angrist
An Evaluation of California's Inmate Classification System Using a Generalized Regression Discontinuity Design - Richard Berk and Jan de Leeuw
Minimum Wages and Employment - David Card and Alan Krueger
A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania
Natural and Quasi-Experiments in Economics - Bruce Meyer
How Much Should We Trust Differences-in-Differences Estimates? - Marianne Bertrand et al
A Natural Experiment on Residential Change and Recidivism - David Kirk
Lessons from Hurricane Katrina
Effects of Prenatal Poverty on Infant Health - Kate Strully et al
State-Earned Income Tax Credits and Birth Weight
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Review quote

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 * 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 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 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 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. 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 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
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About Peter Davis

Peter Davis is Director of the COMPASS Research Centre and Professor of Sociology at the University of Auckland, with cross-appointments in the School of Population Health and in the Department of Statistics, also at the University of Auckland. Previously he served as Professor of Public Health at the University of Otago's Christchurch School of Medicine. Davis specialises in medical sociology, and has achieved international recognition in his field, having worked as a consultant for the World Health Organisation. His main interests are in research methods, social structures, and policy, particularly health policy and health services. He has collaborated with colleagues in health research and in social statistics on a number of major surveys since the 1970s. He was Senior Editor (Health Policy) at the international journal, Social Science and Medicine, until 2012.
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