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Statistical Models and Causal Inference
A Dialogue with the Social Sciences

$124.00 (P)

  • Date Published: November 2009
  • availability: Available
  • format: Hardback
  • isbn: 9780521195003

$ 124.00 (P)
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About the Authors
  • David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the foundations and limitations of statistical modeling, illustrating basic arguments with examples from political science, public policy, law, and epidemiology. Freedman maintains that many new technical approaches to statistical modeling constitute not progress, but regress. Instead, he advocates a “shoe leather” methodology, which exploits natural variation to mitigate confounding and relies on intimate knowledge of the subject matter to develop meticulous research designs and eliminate rival explanations. When Freedman first enunciated this position, he was met with skepticism, in part because it was hard to believe that a mathematical statistician of his stature would favor “low-tech” approaches. But the tide is turning. Many social scientists now agree that statistical technique cannot substitute for good research design and subject matter knowledge. This book offers an integrated presentation of Freedman’s views.

    • Collects in one place many of Freedman's most important works
    • Freedman's work challenges the assumptions of statistical research in social science, public policy, law, and epidemiology
    • Addresses issues of current concern, including swine flu vaccine, earthquake prediction, and statistical adjustments to the US census
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    Product details

    • Date Published: November 2009
    • format: Hardback
    • isbn: 9780521195003
    • length: 416 pages
    • dimensions: 244 x 168 x 25 mm
    • weight: 0.75kg
    • availability: Available
  • Table of Contents

    Editors' introduction: inference and shoe leather
    Part I. Statistical Modeling: Foundations and Limitations:
    1. Some issues in the foundations of statistics: probability and model validation
    2. Statistical assumptions as empirical commitments
    3. Statistical models and shoe leather
    Part II. Studies in Political Science, Public Policy, and Epidemiology:
    4. Methods for Census 2000 and statistical adjustments
    5. On 'solutions' to the ecological inference problem
    6. Rejoinder to King
    7. Black ravens, white shoes, and case selection: inference with categorical variables
    8. What is the chance of an earthquake?
    9. Salt and blood pressure: conventional wisdom reconsidered
    10. The Swine Flu vaccine and Guillain-Barré Syndrome: relative risk and specific causation
    11. Survival analysis: an epidemiological hazard?
    Part III. New Developments: Progress or Regress?:
    12. On regression adjustments in experiments with several treatments
    13. Randomization does not justify logistic regression
    14. The grand leap
    15. On specifying graphical models for causation, and the identification problem
    16. Weighting regressions by propensity scores
    17. On the so-called 'Huber sandwich estimator' and 'robust standard errors'
    18. Endogeneity in probit response models
    19. Diagnostics cannot have much power against general alternatives
    Part IV. Shoe Leather, Revisited:
    20. On types of scientific inquiry: the role of quantitative reasoning.

  • Instructors have used or reviewed this title for the following courses

    • Advanced Quantitative Methods I
    • Advanced Regression and Program Evaluation Methods
    • Advanced Statistical Modeling
    • Analyse quantitative
    • Applied Quantitative Data Analysis
    • Canadian Political Science: A Survey of the Field
    • Causality and graphical Markov models
    • Data Analysis 1
    • Evaluation Research
    • Intermediate Statistics for social and behavioral sciences
    • Methods for Cross-National Research
    • Probability & Statistics
    • Qualitative Methods and Research Design
    • Research Methods ll
    • Social Statistics Part ll
    • Special Topices in Contemporary American Politics
    • Statistical Methods ll
    • Undergraduate Statistics
    • Uses of Intermediate Statistical Methods in Political Science
  • Author

    David A. Freedman
    David A. Freedman (1938–2008) was Professor of Statistics at the University of California, Berkeley. He was a distinguished mathematical statistician whose theoretical research included the analysis of martingale inequalities, Markov processes, de Finetti's theorem, consistency of Bayes estimators, sampling, the bootstrap, and procedures for testing and evaluating models of methods for causal inference. Freedman published widely on the application - and misapplication - of statistics in works within a variety of social sciences, including epidemiology, demography, political science, public policy, and law. He emphasized exposing and checking the assumptions that underlie standard methods, as well as understanding how those methods behave when the assumptions are false - for example, how regression models behave when fitted to data from randomized experiments. He had a remarkable talent for integrating carefully honed statistical arguments with compelling empirical applications and illustrations. Freedman was a member of the American Academy of Arts and Sciences, and in 2003 he received the National Academy of Science's John J. Carty Award, for his 'profound contributions to the theory and practice of statistics'.

    Editors

    David Collier, University of California, Berkeley
    David Collier is Robson Professor of Political Science at University of California, Berkeley. His current work focuses on conceptualization and measurement and on causal inference in qualitative and multi-method research. He is co-author of Rethinking Social Inquiry: Diverse Tools, Shared Standards and co-editor of two recent volumes: The Oxford Handbook of Political Methodology and Concepts and Method in Social Science. A central concern of his research is with the contribution of detailed case knowledge to causal inference and concept formation.

    Jasjeet S. Sekhon, University of California, Berkeley
    Jasjeet S. Sekhon is Associate Professor of Political Science at University of California, Berkeley. His current research focuses on methods for causal inference in observational and experimental studies and evaluating social science, public health, and medical interventions. Professor Sekhon has done research on elections, voting behavior, and public opinion in the United States; multivariate matching methods for causal inference; machine learning algorithms for irregular optimization problems; robust estimators with bounded influence functions; health economic cost effectiveness analysis; and the philosophy and history of inference and statistics in the social sciences.

    Philip B. Stark, University of California, Berkeley
    Philip B. Stark is Professor of Statistics at University of California, Berkeley. His research centers on inference problems, focusing on quantifying the uncertainty in inferences that rely on numerical models of complex physical systems. Professor Stark has done research on the Big Bang, causal inference, the US census, chemical spectroscopy, earthquake prediction, election auditing, food web models, the geomagnetic field, geriatric hearing loss, information retrieval, Internet content filters, nonparametrics, the seismic structure of the Earth and Sun, and spectrum estimation. He has served as an expert witness on statistics for government and industry in state and federal courts.

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