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Statistical Models and Causal Inference

Book Description

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 scepticism, 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.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Contents
  5. Preface
  6. Editors’ Introduction: Inference and Shoe Leather
  7. Part I Statistical Modeling: Foundations and Limitations
    1. Chapter 1. Issues in the Foundations of Statistics: Probability and Statistical Models
    2. Chapter 2. Statistical Assumptions as Empirical Commitments
    3. Chapter 3. Statistical Models and Shoe Leather
  8. Part II Studies in Political Science, Public Policy, and Epidemiology
    1. Chapter 4. Methods for Census 2000 and Statistical Adjustments
    2. Chapter 5. On “Solutions” to the Ecological Inference Problem
    3. Chapter 6. Rejoinder to King
    4. Chapter 7. Black Ravens, White Shoes, and Case Selection: Inference with Categorical Variables
    5. Chapter 8. What is the Chance of an Earthquake?
    6. Chapter 9. Salt and Blood Pressure: Conventional Wisdom Reconsidered
    7. Chapter 10. The Swine Flu Vaccine and Guillain-Barré Syndrome: A Case Study in Relative Risk and Specific Causation
    8. Chapter 11. Survival Analysis: An Epidemiological Hazard?
  9. Part III New Developments: Progress or Regress?
    1. Chapter 12. On Regression Adjustments in Experiments with Several Treatments
    2. Chapter 13. Randomization Does Not Justify Logistic Regression
    3. Chapter 14. The Grand Leap
    4. Chapter 15. On Specifying Graphical Models for Causation, and the Identification Problem
    5. Chapter 16. Weighting Regressions by Propensity Scores
    6. Chapter 17. On the So-Called “Huber Sandwich Estimator” and “Robust Standard Errors”
    7. Chapter 18. Endogeneity in Probit Response Models
    8. Chapter 19. Diagnostics Cannot Have Much Power Against General Alternatives
  10. Part IV Shoe Leather Revisited
    1. Chapter 20. On Types of Scientific Inquiry: The Role of Qualitative Reasoning
  11. References and Further Reading
  12. Index