Data Inference in Observational Settings

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.

VOLUME ONE: BACKGROUND PART ONE: CAUSAL INFERENCE FROM OBSERVATIONAL DATA 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 PART TWO: POTENTIAL OUTCOMES AND COUNTERFACTUALS 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 PART THREE: PROGRAMME AND POLICY EVALUATION 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 VOLUME TWO: ANALYTICAL TECHNIQUES PART FOUR: MATCHING METHODS 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 PART FIVE: PROPENSITY SCORING 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 PART SIX: CAUSAL DIAGRAMS 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 VOLUME THREE: TEMPORAL RELATIONS PART SEVEN: PANEL STUDIES 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 PART EIGHT: FAMILY STUDIES 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 PART NINE: INSTRUMENTAL VARIABLES 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 VOLUME FOUR: EXPERIMENTAL ANALOGUES PART TEN: THE EXPERIMENTAL PARADIGM 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 PART ELEVEN: REGRESSION DISCONTINUITY 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 PART TWELVE: QUASI-EXPERIMENTS AND NATURAL EXPERIMENTS 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

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 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 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. 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 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 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 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 *