Data Inference in Observational Settings
Number Of Volumes: 4
Purchase
Hardcover
ISBN:
9781446266502
Available from
January 0001
Description
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
Contents
VOLUME ONE: BACKGROUND
VOLUME ONE: BACKGROUND
PART ONE: CAUSAL INFERENCE FROM OBSERVATIONAL DATA
- Estimating Causal Effects of Treatments in Randomized and Non-Randomized Studies
- Statistics and Causal Inference
- Misunderstandings between Experimentalists and Observationalists about Causal Inference
- The Estimation of Causal Effects from Observational Data
- Causal Inferences in Sociological Research
PART TWO: POTENTIAL OUTCOMES AND COUNTERFACTUALS
- On the Application of Probability Theory to Agricultural Experiments
- Essay on Principles: Section Nine
- Causal Inference Using Potential Outcomes
- Design, Modeling, Decisions
- Counterfactuals and Hypothesis-Testing in Political Science
- Counterfactuals, Causal Effect Heterogeneity and the Catholic School Effect on Learning
- Does Marriage Reduce Crime? A Counterfactual Approach to within-Individual Causal Effects
PART THREE: PROGRAMME AND POLICY EVALUATION
- Reforms as Experiments
- Evaluating the Econometric Evaluations of Training Programs with Experimental Data
- Choosing among Alternative Non-Experimental Methods for Estimating the Impact of Social Programs
- The Case of Manpower Training
- Estimating the Effects of Potential Public Health Interventions on Population Disease Burden
- A Step-by-Step Illustration of Causal Inference Methods
- The Credibility Revolution in Empirical Economics
- 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
- Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
- Matching with Multiple Controls to Estimate Treatment Effects in Observational Studies
- Matching Estimators of Causal Effects
- Prospects and Pitfalls in Theory and Practice
- Matching Methods for Causal Inference
- A Review and a Look forward
PART FIVE: PROPENSITY SCORING
- The Central Role of the Propensity Score in Observational Studies for Causal Effects
- Propensity Score-Matching Methods for Non-Experimental Causal Studies
- Too Much Ado about Propensity Score Models? Comparing Methods of Propensity Score Matching
- A Comparison of the Ability of Different Propensity Score Models to Balance Measured Variables between Treated and Untreated Subjects
- A Monte Carlo Study
- Selection Bias in Web Surveys and the Use of Propensity Scores
PART SIX: CAUSAL DIAGRAMS
- Correlation and Causation
- Structural Equation Methods in the Social Sciences
- Causal Diagrams for Empirical Research
- From Causal Diagrams to Birth Weight-Specific Curves of Infant Mortality
- Neighborhood Effects in Temporal Perspective
- 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
- Panel Data to Estimate Effects of Events
- The Impact of Incarceration on Wage Mobility and Inequality
- Panel Models in Sociological Research
- Theory into Practice
- Correlation or Causation? Income Inequality and Infant Mortality in Fixed Effects Models in the Period 1960-2008 in 34 OECD Countries
PART EIGHT: FAMILY STUDIES
- Sibling Models and Data in Economics
- Beginnings of a Survey
- Fraternal Resemblance in Education Attainment and Occupational Status
- Is Biology Destiny? Birth Weight and Life Chances
- Schooling or Social Origin? The Bias in the Effect of Educational Attainment on Social Orientations
- Social Science Methods for Twins Data
- 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
- Identification of Causal Effects Using Instrumental Variables
- The Colonial Origins of Comparative Development
- An Empirical Investigation
- A Genetic Instrumental Variables Analysis of the Effects of Prenatal Smoking on Birth Weight
- Evidence from Two Samples
- Instrumental Variables in Sociology and the Social Sciences
- VOLUME FOUR: EXPERIMENTAL ANALOGUES
PART TEN: THE EXPERIMENTAL PARADIGM
- Causal Inference from Randomized Trials in Social Epidemiology
- What Do Randomised Studies of Housing Mobility Demonstrate? Causal Inference in the Face of Interference
- Three Conditions under Which Experiments and Observational Studies Produce Comparable Causal Estimates
- New Findings from within-Study Comparisons
- The Impact of Elections on Co-peration
- Evidence from a Lab-in-the-Field Experiment in Uganda
- Neighborhood Effects on Long-Term Well-Being of Low-Income Adults
PART ELEVEN: REGRESSION DISCONTINUITY
- Regression-Discontinuity Analysis
- An alternative to the ex post facto Experiment
- Assignment to a Treatment Group on the Basis of a Covariate
- Capitalizing on Non-Random Assignment to Treatments
- A Regression-Discontinuity Evaluation of a Crime-Control Program
- Identification and Estimation of Local Average Treatment Effects
- An Evaluation of California's Inmate Classification System Using a Generalized Regression Discontinuity Design
PART TWELVE: QUASI-EXPERIMENTS AND NATURAL EXPERIMENTS
- Minimum Wages and Employment
- A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania
- Natural and Quasi-Experiments in Economics
- How Much Should We Trust Differences-in-Differences Estimates?
- A Natural Experiment on Residential Change and Recidivism
- Lessons from Hurricane Katrina
- Effects of Prenatal Poverty on Infant Health
- State-Earned Income Tax Credits and Birth Weight
Description
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
Contents
VOLUME ONE: BACKGROUND
VOLUME ONE: BACKGROUND
PART ONE: CAUSAL INFERENCE FROM OBSERVATIONAL DATA
- Estimating Causal Effects of Treatments in Randomized and Non-Randomized Studies
- Statistics and Causal Inference
- Misunderstandings between Experimentalists and Observationalists about Causal Inference
- The Estimation of Causal Effects from Observational Data
- Causal Inferences in Sociological Research
PART TWO: POTENTIAL OUTCOMES AND COUNTERFACTUALS
- On the Application of Probability Theory to Agricultural Experiments
- Essay on Principles: Section Nine
- Causal Inference Using Potential Outcomes
- Design, Modeling, Decisions
- Counterfactuals and Hypothesis-Testing in Political Science
- Counterfactuals, Causal Effect Heterogeneity and the Catholic School Effect on Learning
- Does Marriage Reduce Crime? A Counterfactual Approach to within-Individual Causal Effects
PART THREE: PROGRAMME AND POLICY EVALUATION
- Reforms as Experiments
- Evaluating the Econometric Evaluations of Training Programs with Experimental Data
- Choosing among Alternative Non-Experimental Methods for Estimating the Impact of Social Programs
- The Case of Manpower Training
- Estimating the Effects of Potential Public Health Interventions on Population Disease Burden
- A Step-by-Step Illustration of Causal Inference Methods
- The Credibility Revolution in Empirical Economics
- 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
- Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
- Matching with Multiple Controls to Estimate Treatment Effects in Observational Studies
- Matching Estimators of Causal Effects
- Prospects and Pitfalls in Theory and Practice
- Matching Methods for Causal Inference
- A Review and a Look forward
PART FIVE: PROPENSITY SCORING
- The Central Role of the Propensity Score in Observational Studies for Causal Effects
- Propensity Score-Matching Methods for Non-Experimental Causal Studies
- Too Much Ado about Propensity Score Models? Comparing Methods of Propensity Score Matching
- A Comparison of the Ability of Different Propensity Score Models to Balance Measured Variables between Treated and Untreated Subjects
- A Monte Carlo Study
- Selection Bias in Web Surveys and the Use of Propensity Scores
PART SIX: CAUSAL DIAGRAMS
- Correlation and Causation
- Structural Equation Methods in the Social Sciences
- Causal Diagrams for Empirical Research
- From Causal Diagrams to Birth Weight-Specific Curves of Infant Mortality
- Neighborhood Effects in Temporal Perspective
- 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
- Panel Data to Estimate Effects of Events
- The Impact of Incarceration on Wage Mobility and Inequality
- Panel Models in Sociological Research
- Theory into Practice
- Correlation or Causation? Income Inequality and Infant Mortality in Fixed Effects Models in the Period 1960-2008 in 34 OECD Countries
PART EIGHT: FAMILY STUDIES
- Sibling Models and Data in Economics
- Beginnings of a Survey
- Fraternal Resemblance in Education Attainment and Occupational Status
- Is Biology Destiny? Birth Weight and Life Chances
- Schooling or Social Origin? The Bias in the Effect of Educational Attainment on Social Orientations
- Social Science Methods for Twins Data
- 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
- Identification of Causal Effects Using Instrumental Variables
- The Colonial Origins of Comparative Development
- An Empirical Investigation
- A Genetic Instrumental Variables Analysis of the Effects of Prenatal Smoking on Birth Weight
- Evidence from Two Samples
- Instrumental Variables in Sociology and the Social Sciences
- VOLUME FOUR: EXPERIMENTAL ANALOGUES
PART TEN: THE EXPERIMENTAL PARADIGM
- Causal Inference from Randomized Trials in Social Epidemiology
- What Do Randomised Studies of Housing Mobility Demonstrate? Causal Inference in the Face of Interference
- Three Conditions under Which Experiments and Observational Studies Produce Comparable Causal Estimates
- New Findings from within-Study Comparisons
- The Impact of Elections on Co-peration
- Evidence from a Lab-in-the-Field Experiment in Uganda
- Neighborhood Effects on Long-Term Well-Being of Low-Income Adults
PART ELEVEN: REGRESSION DISCONTINUITY
- Regression-Discontinuity Analysis
- An alternative to the ex post facto Experiment
- Assignment to a Treatment Group on the Basis of a Covariate
- Capitalizing on Non-Random Assignment to Treatments
- A Regression-Discontinuity Evaluation of a Crime-Control Program
- Identification and Estimation of Local Average Treatment Effects
- An Evaluation of California's Inmate Classification System Using a Generalized Regression Discontinuity Design
PART TWELVE: QUASI-EXPERIMENTS AND NATURAL EXPERIMENTS
- Minimum Wages and Employment
- A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania
- Natural and Quasi-Experiments in Economics
- How Much Should We Trust Differences-in-Differences Estimates?
- A Natural Experiment on Residential Change and Recidivism
- Lessons from Hurricane Katrina
- Effects of Prenatal Poverty on Infant Health
- State-Earned Income Tax Credits and Birth Weight
Reviews
December 2013 | 1648 pages | Sage UK
| Format | Published Date | ISBN | Price |
|---|---|---|---|
| Hardcover | 31/03/2026 | 9781446266502 | $1307.00 |
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
Table Of Contents:
- VOLUME ONE: BACKGROUND
- PART ONE: CAUSAL INFERENCE FROM OBSERVATIONAL DATA
- Estimating Causal Effects of Treatments in Randomized and Non-Randomized Studies
- Statistics and Causal Inference
- Misunderstandings between Experimentalists and Observationalists about Causal Inference
- The Estimation of Causal Effects from Observational Data
- Causal Inferences in Sociological Research
- PART TWO: POTENTIAL OUTCOMES AND COUNTERFACTUALS
- On the Application of Probability Theory to Agricultural Experiments
- Essay on Principles: Section Nine
- Causal Inference Using Potential Outcomes
- Design, Modeling, Decisions
- Counterfactuals and Hypothesis-Testing in Political Science
- Counterfactuals, Causal Effect Heterogeneity and the Catholic School Effect on Learning
- Does Marriage Reduce Crime? A Counterfactual Approach to within-Individual Causal Effects
- PART THREE: PROGRAMME AND POLICY EVALUATION
- Reforms as Experiments
- Evaluating the Econometric Evaluations of Training Programs with Experimental Data
- Choosing among Alternative Non-Experimental Methods for Estimating the Impact of Social Programs
- The Case of Manpower Training
- Estimating the Effects of Potential Public Health Interventions on Population Disease Burden
- A Step-by-Step Illustration of Causal Inference Methods
- The Credibility Revolution in Empirical Economics
- 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
- Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
- Matching with Multiple Controls to Estimate Treatment Effects in Observational Studies
- Matching Estimators of Causal Effects
- Prospects and Pitfalls in Theory and Practice
- Matching Methods for Causal Inference
- A Review and a Look forward
- PART FIVE: PROPENSITY SCORING
- The Central Role of the Propensity Score in Observational Studies for Causal Effects
- Propensity Score-Matching Methods for Non-Experimental Causal Studies
- Too Much Ado about Propensity Score Models? Comparing Methods of Propensity Score Matching
- A Comparison of the Ability of Different Propensity Score Models to Balance Measured Variables between Treated and Untreated Subjects
- A Monte Carlo Study
- Selection Bias in Web Surveys and the Use of Propensity Scores
- PART SIX: CAUSAL DIAGRAMS
- Correlation and Causation
- Structural Equation Methods in the Social Sciences
- Causal Diagrams for Empirical Research
- From Causal Diagrams to Birth Weight-Specific Curves of Infant Mortality
- Neighborhood Effects in Temporal Perspective
- 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
- Panel Data to Estimate Effects of Events
- The Impact of Incarceration on Wage Mobility and Inequality
- Panel Models in Sociological Research
- Theory into Practice
- Correlation or Causation? Income Inequality and Infant Mortality in Fixed Effects Models in the Period 1960-2008 in 34 OECD Countries
- PART EIGHT: FAMILY STUDIES
- Sibling Models and Data in Economics
- Beginnings of a Survey
- Fraternal Resemblance in Education Attainment and Occupational Status
- Is Biology Destiny? Birth Weight and Life Chances
- Schooling or Social Origin? The Bias in the Effect of Educational Attainment on Social Orientations
- Social Science Methods for Twins Data
- 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
- Identification of Causal Effects Using Instrumental Variables
- The Colonial Origins of Comparative Development
- An Empirical Investigation
- A Genetic Instrumental Variables Analysis of the Effects of Prenatal Smoking on Birth Weight
- Evidence from Two Samples
- Instrumental Variables in Sociology and the Social Sciences
- VOLUME FOUR: EXPERIMENTAL ANALOGUES
- PART TEN: THE EXPERIMENTAL PARADIGM
- Causal Inference from Randomized Trials in Social Epidemiology
- What Do Randomised Studies of Housing Mobility Demonstrate? Causal Inference in the Face of Interference
- Three Conditions under Which Experiments and Observational Studies Produce Comparable Causal Estimates
- New Findings from within-Study Comparisons
- The Impact of Elections on Co-peration
- Evidence from a Lab-in-the-Field Experiment in Uganda
- Neighborhood Effects on Long-Term Well-Being of Low-Income Adults
- PART ELEVEN: REGRESSION DISCONTINUITY
- Regression-Discontinuity Analysis
- An alternative to the ex post facto Experiment
- Assignment to a Treatment Group on the Basis of a Covariate
- Capitalizing on Non-Random Assignment to Treatments
- A Regression-Discontinuity Evaluation of a Crime-Control Program
- Identification and Estimation of Local Average Treatment Effects
- An Evaluation of California's Inmate Classification System Using a Generalized Regression Discontinuity Design
- PART TWELVE: QUASI-EXPERIMENTS AND NATURAL EXPERIMENTS
- Minimum Wages and Employment
- A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania
- Natural and Quasi-Experiments in Economics
- How Much Should We Trust Differences-in-Differences Estimates?
- A Natural Experiment on Residential Change and Recidivism
- Lessons from Hurricane Katrina
- Effects of Prenatal Poverty on Infant Health
- State-Earned Income Tax Credits and Birth Weight
Recent Product Reviews:
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.
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
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