Causal inference in the health sciences: A conceptual introduction

Health Services and Outcomes Research Methodology

Volumn 2, Issue 3-4, 2001, Pages 189-220

  Pearl, Judea ^a  

^a University of California   (United States)

Author keywords

Confounding; Counterfactuals; Graphical methods; Noncompliance; Structural equation models

Indexed keywords

ANALYTIC METHOD; ARTICLE; CLINICAL STUDY; CONTROLLED STUDY; EXPERIMENTATION; HEALTH SCIENCE; HEALTH SERVICES RESEARCH; HUMAN; MATHEMATICAL COMPUTING; MULTIVARIATE ANALYSIS; NOMENCLATURE; PATIENT COMPLIANCE; PRIORITY JOURNAL; STATISTICAL ANALYSIS;

EID: 0035761764     PISSN: 13873741     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1020315127304     Document Type: Article

References (72)

1. J. D. Angrist, G. W. Imbens and D. B. Rubin, "Identification of causal effects using instrumental variables (with comments)," Journal of the American Statistical Association, 91(434), pp. 4444-72, 1996.
2. A. Balke and J. Pearl, "Counterfactuals and policy analysis in structural models," in Uncertainty in Artificial Intelligence 11 (P Besnard and S. Hanks, eds.), Morgan Kaufmann, San Francisco, pp. 11-18, 1995.
3. A. Balke and J. Pearl, "Bounds on treatment effects from studies with imperfect compliance," Journal of the American Statistical Association, 92(439), pp. 1172-1176, 1997.
4. H. Becher, "The concept of residual confounding in regression models and some applications," Statistics in Medicine, 11, pp. 1747-1758, 1992.
5. Y. M. M. Bishop, "Effects of collapsing multidimensional contingency tables," Biometrics, 27, pp. 545-562, 1971.
6. K. A. Bollen. Structural Equations with Latent Variables, John Wiley, New York, 1989.
7. B. Bonet, "Instrumentality tests revisited," in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp. 48-55, 2001.
8. R. J. Bowden and D. A. Turkington. Instrumental Variables, Cambridge University Press, Cambridge, England, 1984.
9. N. E. Breslow and N. E. Day. Statistical Methods in Cancer Research; Vol. 1, The Analysis of Case-Control Studies, IARC, Lyon, 1980.
10. N. Cartwright. Nature's Capacities and Their Measurement, Clarendon Press, Oxford, 1989.
11. D. M. Chickering and J. Pearl, "A clinician's tool for analyzing non-compliance," Computing Science and Statistics, 29(2), pp. 424-431, 1997.
12. R. G. Cowell, A. R Dawid, S. L. Lauritzen and D. J. Spielgelhalter. Probabilistic Networks and Expert Systems, Springer Verlag, New York, NY, 1999.
13. D. R. Cox. The Planning of Experiments, John Wiley and Sons, NY, 1958.
14. A. P. Dawid, "Conditional independence in statistical theory," Journal of the Royal Statistical Society, Series B, 41(1), pp. 1-31, 1979.
15. O. D. Duncan. Introduction to Structural Equation Models, Academic Press, New York, 1975.
16. E. Eells. Probabilistic Causality, Cambridge University Press, Cambridge, MA, 1991.
17. D. Freedman, "As others see us: A case study in path analysis (with discussion)," Journal of Educational Statistics, 12(2), pp. 101-223, 1987.
18. D. Galles and J. Pearl, "Testing identifiability of causal effects," in Uncertainty in Artificial Intelligence 11 (P. Besnard and S. Hanks, eds.), Morgan Kaufmann, San Francisco, pp. 185-195, 1995.
19. A. S. Goldberger, "Structural equation models in the social sciences," Econometrica: Journal of the Econometric Society, 40, pp. 979-1001, 1972.
20. D. A. Grayson, "Confounding confounding," American Journal of Epidemiology, 126, pp. 546-553, 1987.
21. S. Greenland and J. M. Robins, "Identifiability, exchangeability, and epidemiological confounding," International Journal of Epidemiology, 15(3), pp. 413-419, 1986.
22. S. Greenland, J. Pearl and J. M. Robins, "Causal diagrams for epidemiologic research," Epidemiology, 10(1), pp. 37-48, 1999a.
23. S. Greenland, J. M. Robins and J. Pearl, "Confounding and collapsibility in causal inference," Statistical Science, 14(1), pp. 29-46, 1999b.
24. S. Greenland, "Relation of the probability of causation to the relative risk and the doubling dose: A methodologic error that has become a social problem. American Journal of Public Health, 89, pp. 1166-1169, 1999.
25. W. W. Hauck, J. M. Heuhaus, J. D. Kalbfleisch and S. Anderson, "A consequence of omitted covariates when estimating odds ratios," Journal Clinical Epidemiology, 44(1), pp. 77-81, 1991.
26. J. J. Heckman and J. Smith, "Evaluating the welfare state," in Econometric and Economic Theory in the 20th Century (S. Strom, ed.), Cambridge University Press, Cambridge, England, pp. 1-60, 1998.
27. P. W. Holland and D. B. Rubin, "Causal inference in retrospective studies," Evaluation Review, 13, pp. 203-231, 1988.
28. P. W. Holland, "Causal inference, path analysis, and recursive structural equations models," in Sociological Methodology (C. Clogg, ed.), American Sociological Association, Washington, D.C., pp. 449-484, 1988.
29. G. W. Imbens and J. D. Angrist, "Identification and estimation of local average treatment effects," Econometrica, 62(2), pp. 467-475, 1994.
30. K. G. Joreskog and D. Sorbom. LISREL IV: Analysis of Linear Structural Relationships by Maximum Likelihood, International Educational Services, Chicago, 1978.
31. J. S. Kaufman and S. Kaufman, "Assessment of structured socioeconomic effects on health," Epidemiology, 12(2), pp. 157-167, 2001.
32. H. Kiiveri, T. R Speed and J. B. Carlin, "Recursive causal models," Journal of Australian Math Society, 36, pp. 30-52, 1984.
33. D. G. Kleinbaum, L. L. Kupper, K. E. Muller and A. Nizam. Applied Regression Analysis and Other Multivariable Methods, Duxbury Press, Pacific Grove, third edition, 1998.
34. T. C. Koopmans, "Identification problems in econometric model construction," in Studies in Econometric Method (W C. Hood and T. C. Koopmans, eds.), Wiley, New York, pp. 27-48, 1953.
35. M. Kuroki and M. Miyakawa, "Identifiability criteria for causal effects of joint interventions," Journal of the Japan Statistical Society, 29(2), pp. 105-117, 1999.
36. S. L. Lauritzen. Graphical Models, Clarendon Press, Oxford, 1996.
37. S. L. Lauritzen, "Causal inference from graphical models," Technical Report R-99-2021, Department of Mathematical Sciences, Aalborg University, Denmark, 1999.
38. D. V. Lindley and M. R. Novick, "The role of exchangeability in inference," The Annals of Statistics, 9(1), pp. 45-58, 1981.
39. C. F. Manski, "Nonparametric bounds on treatment effects," American Economic Review, Papers and Proceedings, 80, pp. 319-323, 1990.
40. C. F. Manski. Identification Problems in the Social Sciences,, Harvard University Press, Cambridge, MA, 1995.
41. O. S. Miettinen and E. F. Cook, "Confounding essence and detection," American Journal of Epidemiology, 114, pp. 593-603, 1981.
42. B. Muthen, "Response to Freedman's critique of path analysis: Improve credibility by better methodological training," Journal of Educational Statistics, 12(2), pp. 178-184, 1987.
43. J. Neyman, "On the application of probability theory to agricultural experiments," Essay on principles. Section 9. Statistical Science, 5(4), pp. 465-480, 1923.
44. J. Pearl and J. M. Robins, "Probabilistic evaluation of sequential plans from causal models with hidden variables," in Uncertainty in Artificial Intelligence 11 (P. Besnard and S. Hanks, eds.), Morgan Kaufmann, San Francisco, pp. 444-453, 1995.
45. J. Pearl and T. Verma, "A theory of inferred causation," in Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference (J. A. Allen, R. Fikes and E. Sandewall, eds.), Morgan Kaufmann, San Mateo, CA, pp. 441-452, 1991.
46. J. Pearl. Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann, San Mateo, CA, 1988.
47. J. Pearl, "Comment: Graphical models, causality, and intervention," Statistical Science, 8, pp. 266-269, 1993.
48. J. Pearl, "Causal diagrams for empirical research," Biometrika, 82(4), pp. 669-710, 1995a.
49. J. Pearl, "On the testability of causal models with latent and instrumental variables," in Uncertainty in Artificial Intelligence 11 (P. Besnard and S. Hanks, eds.), Morgan Kaufmann, pp. 435-443, 1995b.
50. J. Pearl. Causality: Models, Reasoning, and Inference, Cambridge University Press, New York, 2000.
51. J. M. Robins, "The analysis of randomized and non-randomized aids treatments trials using anew approach to casual inference in longitudinal studies," in Health Service Research Methodology: A Focus on AIDS (L. Sechrest, H. Freeman, and A. Mulley, eds.), U.S. Public Health Service, Washington D.C., pp. 113-159, 1989a.
52. J. M. Robins and S. Greenland, "The probability of causation under a stochastic model for individual risk," Biometrics, 45, pp. 1125-1138, 1989b.
53. J. M. Robins and S. Greenland, "Identifiability and exchangeability for direct and indirect effects," Epidemiology, 3(2), pp. 143-155, 1992.
54. J. M. Robins, "A new approach to causal inference in mortality studies with a sustained exposure period-applications to control of the healthy workers survivor effect," Mathematical Modeling, 7, pp. 1393-1512, 1986.
55. J. M. Robins, "A graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods," Journal of Chronic Diseases, 40(Suppl 2), pp. 139S-161S, 1987.
56. J. M. Robins, "Data, design, and background knowledge in etiologic inference," Epidemiology, 12(3), pp. 313-320, 2001.
57. P. Rosenbaum and D. Rubin, "The central role of propensity score in observational studies for causal effects," Biometrica, 70, pp. 41-55, 1983.
58. D. B. Rubin, "Estimating causal effects of treatments in randomized and nonrandomized studies," Journal of Educational Psychology, 66, pp. 688-701, 1974.
59. H. A. Simon and N. Rescher, "Cause and counterfactual," Philosophy and Science, 33, pp. 323-340, 1966.
60. H. A. Simon, "Causal ordering and identifiability," in Studies in Econometric Method (Wm. C. Hood and T. C. Koopmans, eds.), Wiley and Sons Inc., pp. 49-74, 1953.
61. M. E. Sobel, "Causal inference in statistical models of the process of socioeconomic achievement," Sociological Methods & Research, 27(2), pp. 318-348, 1998.
62. P. Spirtes, C. Glymour and R. Scheines. Causation, Prediction, and Search, Springer-Verlag, New York, 1993.
63. P. Suppes. A Probabilistic Theory of Causality, North-Holland Publishing Co., Amsterdam, 1970.
64. J. Tian and I Pearl, "Probabilities of causation: Bounds and identification," in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, pp. 589-598, 2000.
65. J. Tian and J. Pearl, "On the identification of causal effects," in Proceedings of the American Association of Artificial Intelligence, AAAI Press/The MIT Press, Menlo Park, CA, 2002.
66. J. Tian, A. Paz and J. Pearl, "Finding minimal separating sets," Technical Report R-254, University of California, Los Angeles, CA, 1998.
67. C. R. Weinberg, "Toward a clearer definition of confounding," American Journal of Epidemiology, 137, pp. 1-8, 1993.
68. N. Wermuth and D. Cox, "Linear dependencies represented by chain graphs," Statistical Science, 8(3), pp. 204-218, 1993.
69. N. Wermuth, "On block-recursive regression equations (with discussion)," Brazilian Journal of Probability and Statistics, 6, pp. 1-56, 1992.
70. J. Whittaker. Graphical Models in Applied Multivariate Statistics, John Wiley, Chichester, England, 1990.
71. A. S. Whittemore, "Collapsibility of multidimensional contingency tables," Journal of the Royal Statistical Society, B, 40(3), pp. 328-340, 1978.
72. S. Wright, "Correlation and causation," Journal of Agricultural Research, 20, pp. 557-585, 1921.