Table of Content:

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-Barr 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.

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 Freedmans views.

http://www.cambridgeindia.org/Academic/subjects/Statistics-and-probability/Statistical-Models-and-Causal-Inference?ISBN=9780521123907