Observational Studies

  • Paul R. Rosenbaum
Part of the Springer Series in Statistics book series (SSS)


William G. Cochran first presented “observational studies” as a topic defined by principles and methods of statistics. Cochran had been an author of the 1964 United States Surgeon General’s Advisory Committee Report, Smoking and Health, which reviewed a vast literature and concluded: “Cigarette smoking is causally related to lung cancer in men; the magnitude of the effect of cigarette smoking far outweighs all other factors. The data for women, though less extensive, point in the same direction (p. 37) .” Though there had been some experiments confined to laboratory animals, the direct evidence linking smoking with human health came from observational or nonexperimental studies.


Observational Study Heavy Smoker Light Smoker Catholic School Bibliographic Note 
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  1. Most scientific fields that study human populations conduct observational studies. Many fields have developed a literature on the design, conduct, and interpretation of observational studies, often with little reference to related work in other fields. It is not possible to do justice to these several literatures in a short bibliographic note. There follows a short and incomplete list of fine books that contain substantial general discussions of the methodology used for observational studies in epidemiology, public program evaluation, or the social sciences. A shared goal in these diverse works is evaluation of treatments, exposures, programs, or policies from nonexperimental data. The list is followed by references cited in Chapter 1.Google Scholar

Some Books and a Few Papers

  1. Angrist, J. D. and Krueger, A. B. (1999) Empirical strategies in labor economics. In: Handbook of Labor Economics, O. Ashenfelter and D. Card, eds., Volume 3A, Chapter 23, New York: Elsevier.Google Scholar
  2. Ashenfelter, O., ed. (2000) Labor Economics. New York: Worth.Google Scholar
  3. Becker, H. S. (1997) Tricks of the Trade. Chicago: University of Chicago Press.Google Scholar
  4. Blaug, M. (1980) The Methodology of Economics. New York: Cambridge University Press.Google Scholar
  5. Breslow, N. and Day, N. (1980, 1987) Statistical Methods in Cancer Research, Volumes 1 and 2. Lyon, France: International Agency for Research on Cancer.Google Scholar
  6. Campbell, D. T. (1988) Methodology and Epistemology for Social Science: Selected Papers. Chicago: University of Chicago Press, pp. 315–333.Google Scholar
  7. Campbell, D. and Stanley, J. (1963) Experimental and Quasi-Experimental Design for Research. Chicago: Rand McNally.Google Scholar
  8. Chamberlain, G. (1984) Panel data. In: Handbook of Econometrics, Chapter 22, Volume 2, Z. Griliches and M. D. Intriligator, eds., New York: Elsevier.Google Scholar
  9. Cochran, W. G. (1965) The planning of observational studies of human populations (with discussion) . Journal of the Royal Statistical Society, Series A, 128, 134–155.Google Scholar
  10. Cochran, W. (1983) Planning and Analysis of Observational Studies. New York WileyCrossRefGoogle Scholar
  11. Cook, T. D. and Campbell, D. C. (1979) Quasi-Experimentation. Chicago: Rand McNally.Google Scholar
  12. Cook, T. D., Campbell, D. T., and Peracchio, L. (1990) Quasi-experimentation. In: Handbook of Industrial and Organizational Psychology, M. Dunnette and L. Hough, eds., Palo Alto, CA: Consulting Psychologists Press, Chapter 9, pp. 491–576.Google Scholar
  13. Cook, T. D. and Shadish, W. R. (1994) Social experiments: Some developments over the past fifteen years. Annual Review of Psychology, 45, 545–580.CrossRefGoogle Scholar
  14. Cornfield, J., Haenszel, W., Hammond, E., Lilienfeld, A., Shimkin, M., and Wynder, E. (1959) Smoking and lung cancer: Recent evidence and a discussion of some questions. Journal of the National Cancer Institute, 22, 173–203.Google Scholar
  15. Cox, D. R. (1992) Causality: Some statistical aspects. Journal of the Royal Statistical Society, Series A, 155, 291–301.zbMATHCrossRefGoogle Scholar
  16. Elwood, J. M. (1988) Causal Relationships in Medicine. New York: Oxford University Press.Google Scholar
  17. Emerson, R. M. (1981) Observational field work. Annual Review of Sociology, 7, 351 378.Google Scholar
  18. Freedman, D. (1997) From association to causation via regression. Advances in Applied Mathematics, 18, 59–110.MathSciNetzbMATHCrossRefGoogle Scholar
  19. Friedman, M. (1953) Essays in Positive Economics. Chicago: University of Chicago Press.Google Scholar
  20. Gastwirth, J. (1988) Statistical Reasoning in Law and Public Policy. New York: Academic Press.zbMATHGoogle Scholar
  21. Gordis, L. (2000) Epidemiology (Second Edition) Philadelphia: Saunders.Google Scholar
  22. Greenhouse, S. (1982) Jerome Cornfield’s contributions to epidemiology. Biometrics, 28, Supplement, 33–46.CrossRefGoogle Scholar
  23. Heckman, J. J. (2001) Micro data, heterogeneity, and the evaluation of public policy: The Nobel lecture. Journal of Political Economy, 109, 673–748.CrossRefGoogle Scholar
  24. Hill, A. B. (1965) The environment and disease: Association or causation? Proceedings of the Royal Society of Medicine, 58, 295–300.Google Scholar
  25. Holland, P. (1986) Statistics and causal inference (with discussion) . Journal of the American Statistical Association, 81, 945–970.MathSciNetzbMATHCrossRefGoogle Scholar
  26. Kelsey, J., Whittemore, A., Evans, A., and Thompson, W. (1996). Methods in Observational Epidemiology. New York: Oxford University Press.Google Scholar
  27. Khoury, M. J., Cohen, B. H., and Beaty, T. H. (1993) Fundamentals of Genetic Epidemiology. New York: Oxford University Press.Google Scholar
  28. Kish, L. (1987) Statistical Design for Research. New York: Wiley.zbMATHCrossRefGoogle Scholar
  29. Lilienfeld, A. and Lilienfeld, D. E. (1980) Foundations of Epidemiology. New York: Oxford University Press.Google Scholar
  30. Lilienfeld, D. E. and Stolley, P. D. (1994) Foundations of Epidemiology. New York: Oxford University Press.Google Scholar
  31. Lipsey, M. W. and Cordray, D. S. (2000) Evaluation methods for social intervention. Annual Review of Psychology, 51, 345–375.CrossRefGoogle Scholar
  32. Little, R. J. and Rubin, D. B. (2000) Causal effects in clinical and epidemiological studies via potential outcomes. Annual Review of Public Health, 21, 121 145.Google Scholar
  33. Maclure, M. and Mittleman, M. A. (2000) Should we use a case-crossover design? Annual Review of Public Health, 21, 193–221.CrossRefGoogle Scholar
  34. MacMahon, B. and Pugh, T. (1970) Epidemiology. Boston: Little, Brown.Google Scholar
  35. MacMahon, B. and Trichopoulos, D. (1996) Epidemiology. Boston: Little, Brown.Google Scholar
  36. Manski, C. (1995) Identification Problems in the Social Sciences. Cambridge, MA: Harvard University Press.Google Scholar
  37. Mantel, N. and Haenszel, W. (1959) Statistical aspects of retrospective studies of disease. Journal of the National Cancer Institute, 22, 719– 748.Google Scholar
  38. Meyer, B. D. (1995) Natural and quasi-experiments in economics. Journal of Business and Economic Statistics, 13, 151–161.Google Scholar
  39. Meyer, M. and Fienberg, S., eds. (1992) Assessing Evaluation Studies: The Case of Bilingual Education Strategies. Washington, DC: National Academy Press.Google Scholar
  40. Miettinen, O. (1985) Theoretical Epidemiology. New York: Wiley.Google Scholar
  41. Pearl, J. (2000) Causality: Models, Reasoning, Inference. New York: Cambridge University Press.Google Scholar
  42. Reichardt, C. S. (2000) A typology of strategies for ruling out threats to validity. In: Research Design: Donald Campbell’s Legacy, L. Brickman, ed., Thousand Oaks, CA: Sage, Volume 2, pp., 89–115.Google Scholar
  43. Reiter, J. (2000) Using statistics to determine causal relationships. American Mathematical Monthly, 107, 2432.MathSciNetCrossRefGoogle Scholar
  44. Robins, J. M. (1999) Association, causation, and marginal structural models. Synthese, 121, 151 179.MathSciNetGoogle Scholar
  45. Robins, J., Blevins, D., Ritter, G., and Wulfsohn, M. (1992) G-estimation of the effect of prophylaxis therapy for pneumocystis carinii pneumonia on the survival of AIDS patients. Epidemiology, 3, 319–336.CrossRefGoogle Scholar
  46. Rosenthal, R. and Rosnow, R., eds. (1969) Artifact in Behavioral Research. New York: Academic.Google Scholar
  47. Rosenzweig, M. R. and Wolpin, K. I. (2000) Natural “natural experiments” in economics. Journal of Economic Literature, 38, 827–874.CrossRefGoogle Scholar
  48. Rosnow, R. L. and Rosenthal, R. (1997) People Studying People: Artifacts and Ethics in Behavioral Research. New York: W. H. Freeman.Google Scholar
  49. Rossi, P., Freeman, H., and Lipsey, M. W. (1999) Evaluation. Beverly Hills, CA: Sage.Google Scholar
  50. Rothman, K. and Greenland, S. (1998) Modern Epidemiology. Philadelphia: Lippincott-Raven.Google Scholar
  51. Rubin, D. (1974) Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.CrossRefGoogle Scholar
  52. Schlesselman, J. (1982) Case-Control Studies. New York: Oxford University Press.Google Scholar
  53. Schulte, P. A. and Perera, F. (1993) Molecular Epidemiology: Principles and Practices. New York: Academic.Google Scholar
  54. Shadish, W. R., Cook, T. D., and Campbell, D. T. (2002) Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston: Houghton-Mifflin.Google Scholar
  55. Shafer, G. (1996) The Art of Causal Conjecture. Cambridge, MA: MIT Press.zbMATHGoogle Scholar
  56. Sobel, M. (1995) Causal inference in the social and behavioral sciences. In: Handbook of Statistical Modelling for the Social and Behavioral Sciences, G. Arminger, C. Clogg, and M. Sobel, eds., New York: Plenum, 1–38.Google Scholar
  57. Steenland, K., ed. (1993) Case Studies in Occupational Epidemiology. New York: Oxford University Press.Google Scholar
  58. Strom, B. (2000) Pharmacoepidemiology. New York: Wiley.CrossRefGoogle Scholar
  59. Suchman, E. (1967) Evaluation Research. New York: Sage.Google Scholar
  60. Susser, M. (1973) Causal Thinking in the Health Sciences: Concepts and Strategies in Epidemiology. New York: Oxford University Press.Google Scholar
  61. Susser, M. (1987) Epidemiology, Health and Society: Selected Papers. New York: Oxford University Press.Google Scholar
  62. Tufte, E., ed. (1970) The Quantitative Analysis of Social Problems. Reading, MA: Addison-Wesley.Google Scholar
  63. Weiss, C. (1997) Evaluation. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  64. Weiss, N. S. (1996) Clinical Epidemiology. New York: Oxford University Press.Google Scholar
  65. Willett, W. (1998) Nutritional Epidemiology. New York: Oxford University Press.CrossRefGoogle Scholar
  66. Winship, C. and Morgan, S. L. (1999) The estimation of causal effects from observational data. Annual Review of Sociology, 25, 659–706.CrossRefGoogle Scholar
  67. Zellner, A. (1968) Readings in Economic Statistics and Econometrics. Boston: Little, Brown.Google Scholar


  1. Bross, I. D. J. (1960) Statistical criticism. Cancer, 13, 394–400CrossRefGoogle Scholar
  2. Bross, I. D. J. (1960) Reprinted in: The Quantitative Analysis of Social Problems, E. Tufte, ed., Reading, MA: Addison-Wesley, pp. 97–108.Google Scholar
  3. Cameron, E. and Pauling, L. (1976) Supplemental ascorbate in the supportive treatment of cancer: Prolongation of survival times in terminal human cancer. Proceedings of the National Academy of Sciences (USA), 73, 3685–3689.CrossRefGoogle Scholar
  4. Chalmers, T., Block, J., and Lee, S. (1970) Controlled studies in clinical cancer research. New England Journal of Medicine, 287, 75–78.CrossRefGoogle Scholar
  5. Cochran, W.G. (1965) The planning of observational studies of human populations (with discussion). Journal of the Royal Statistical Society, Series A, 128, 134–155Google Scholar
  6. Cochran, W.G. (1965) Reprinted in Readings in Economic Statistics and Econometrics, A. Zellner, ed., 1968, Boston: Little Brown, pp. 11–36.Google Scholar
  7. Dehejia, R. H. and Wahba, S. (1999) Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs. Journal of the American Statistical Association, 94, 1053–1062.CrossRefGoogle Scholar
  8. Doll, R. and Hill, A. (1966) Mortality of British doctors in relation to smoking: Observations on coronary thrombosis. In: Epidemiological Approaches to the Study of Cancer and Other Chronic Diseases, W. Haenszel, ed., U.S. National Cancer Institute Monograph 19, Washington, DC: US Department of Health, Education, and Welfare, pp. 205–268.Google Scholar
  9. Fisher, R.A. (1935, 1949) The Design of Experiments. Edinburgh: Oliver & Boyd.Google Scholar
  10. Fraker, T. and Maynard, R. (1987) The adequacy of comparison group designs for evaluations of employment-related programs. Journal of Human Resources, 22, 194–227.CrossRefGoogle Scholar
  11. Friedlander, D. and Robins, P. K. (1995) Evaluating program evaluations: New evidence on commonly used nonexperimental methods. American Economic Review, 85, 923–937.Google Scholar
  12. Gastwirth, J. L., Krieger, A. M., and Rosenbaum, P. R. (1997) Hypotheticals and hypotheses. American Statistician, 51, 120–121.Google Scholar
  13. Herbst, A., Ulfelder, H., and Poskanzer, D. (1971) Adenocarcinoma of the vagina: Association of maternal stilbestrol therapy with tumor appearance in young women. New England Journal of Medicine, 284, 878–881.CrossRefGoogle Scholar
  14. Hoffer, T., Greeley, A., and Coleman, J. (1985 Achievement growth in public and Catholic schools. Sociology of Education, 58, 74–97.CrossRefGoogle Scholar
  15. LaLonde, R. (1986) Evaluating the econometric evaluations of training programs with experimental data. American Economic Review, 76, 604–620.Google Scholar
  16. Meier, P. (1972) The biggest public health experiment ever: The 1954 field trial of the Salk poliomyelitis vaccine. In: Statistics: A Guide to the Unknown, J. Tanur, ed., San Francisco: Holden-Day, pp. 2–13.Google Scholar
  17. Moertel, C., Fleming, T., Creagan, E., Rubin, J., O’Connell, M., and Ames, M. (1985) High-dose vitamin C versus placebo in the treatment of patients with advanced cancer who have had no prior chemotherapy: A randomized double-blind comparison. New England Journal of Medicine, 312, 137–141.CrossRefGoogle Scholar
  18. Popper, K. (1959) The Logic of Scientific Discovery. New York: Harper & Row.zbMATHGoogle Scholar
  19. Popper, K. (1994) The Myth of the Framework. New York: Routledge.Google Scholar
  20. United States Surgeon General’s Advisory Committee Report (1964) Smoking and Health. Washington, DC: US Department of Health, Education and Welfare.Google Scholar
  21. Wittgenstein, L. (1969) On Certainty. New York: Harper & Row.Google Scholar
  22. Zwick, R. (1991) Effects of item order and context on estimation of NAEP reading proficiency. Educational Measurement: Issues and Practice,3, 10–16.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Paul R. Rosenbaum
    • 1
  1. 1.Department of Statistics, The Wharton SchoolUniversity of PennsylvaniaPhiladelphiaUSA

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