skip to main content
chapter

Causal Diagrams for Empirical Research (With Discussions)

Published:04 March 2022Publication History
First page image

References

  1. Angrist, J. D., Imbens, G. W. & Rubin, D. B. (1995). Identification of causal effects using instrumental variables. J. Am. Statist. Assoc. 91, 444–455, 1996.Google ScholarGoogle ScholarCross RefCross Ref
  2. Balke, A. & Pearl, J. (1994). Counterfactual probabilities: Computational methods, bounds, and applications. In Uncertainty in Artificial Intelligence, Ed. R. Lopez de Mantaras and D. Poole, pp. 46–54. San Mateo, CA: Morgan Kaufmann.Google ScholarGoogle Scholar
  3. Bowden, R. J. & Turkington, D. A. (1984). Instrumental Variables. Cambridge, MA: Cambridge University Press.Google ScholarGoogle Scholar
  4. Cox, D. R. (1958). The Planning of Experiments. New York: John Wiley.Google ScholarGoogle Scholar
  5. Cox, D. R. (1992). Causality: Some statistical aspects. J. R. Statist. Soc. A 155, 291–301.Google ScholarGoogle ScholarCross RefCross Ref
  6. Cox, D. R. & Wermuth, N. (1993). Linear dependencies represented by chain graphs. Statist. Sci. 8, 204–18.Google ScholarGoogle ScholarCross RefCross Ref
  7. Dawid, A. P. (1979). Conditional independence in statistical theory (with Discussion). J. R. Statist. Soc. B 41, 1–31.Google ScholarGoogle Scholar
  8. Fisher, F. M. (1970). A correspondence principle for simultaneous equation models. Econometrica 38, 73–92.Google ScholarGoogle ScholarCross RefCross Ref
  9. Freedman, D. (1987). As others see us: A case study in path analysis (with Discussion). J. Educ. Statist. 12, 101–223.Google ScholarGoogle ScholarCross RefCross Ref
  10. Frisch, R. (1938). Statistical versus theoretical relations in economic macrodynamics. League of Nations Memorandum. Reproduced (1948) in Autonomy of Economic Relations, Universitetets Socialokonomiske Institutt, Oslo.Google ScholarGoogle Scholar
  11. Galles, D. & Pearl, J. (1995). Testing identifiability of causal effects. In Uncertainty in Artificial Intelligence—11, Ed. P. Besnard and S. Hanks, pp. 185–95. San Francisco, CA: Morgan Kaufmann.Google ScholarGoogle Scholar
  12. Geiger, D., Verma, T. S. & Pearl, J. (1990). Identifying independence in Bayesian networks. Networks 20, 507–34.Google ScholarGoogle ScholarCross RefCross Ref
  13. Goldberger, A. S. (1972). Structural equation models in the social sciences. Econometrica 40, 979–1001.Google ScholarGoogle ScholarCross RefCross Ref
  14. Haavelmo, T. (1943). The statistical implications of a system of simultaneous equations. Econometrica 11, 112.Google ScholarGoogle ScholarCross RefCross Ref
  15. Holland, P. W. (1988). Causal inference, path analysis, and recursive structural equations models. In Sociological Methodology, Ed. C. Clogg, pp. 449–84. Washington, D.C.: American Sociological Association.Google ScholarGoogle Scholar
  16. Imbens, G. W. & Angrist, J. D. (1994). Identification and estimation of local average treatment effects. Econometrica 62, 467–76.Google ScholarGoogle ScholarCross RefCross Ref
  17. Lauritzen, S. L., Dawid, A. P., Larsen, B. N. & Leimer, H. G. (1990). Independence properties of directed Markov fields. Networks 20, 491–505.Google ScholarGoogle ScholarCross RefCross Ref
  18. Lauritzen, S. L. & Spiegelhalter, D. J. (1988). Local computations with probabilities on graphical structures and their applications to expert systems (with Discussion). J. R. Statist. Soc. B 50, 157–224.Google ScholarGoogle Scholar
  19. Manski, C. F. (1990). Nonparametric bounds on treatment effects. Am. Econ. Rev., Papers Proc. 80, 319–23.Google ScholarGoogle Scholar
  20. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. San Mateo, CA: Morgan Kaufmann.Google ScholarGoogle ScholarCross RefCross Ref
  21. Pearl, J. (1993a). Belief networks revisited. Artif. Intel. 59, 49–56.Google ScholarGoogle ScholarCross RefCross Ref
  22. Pearl, J. (1993b). Comment: Graphical models, causality, and intervention. Statist. Sci. 8, 266-9.Google ScholarGoogle ScholarCross RefCross Ref
  23. Pearl, J. (1994a). From Bayesian networks to causal networks. In Bayesian Networks and Probabilistic Reasoning, Ed. A. Gammerman, pp. 1–31. London: Alfred Walter.Google ScholarGoogle Scholar
  24. Pearl, J. (1994b). A probabilistic calculus of actions. In Uncertainty in Artificial Intelligence, Ed. R. Lopez de Mantaras and D. Poole, pp. 452–62. San Mateo, CA: Morgan Kaufmann.Google ScholarGoogle Scholar
  25. Pearl, J. (1995). Causal inference from indirect experiments. Artif. Intel. Med. J., 7, 561–582, 1995.Google ScholarGoogle ScholarCross RefCross Ref
  26. Pearl, J. & Verma, T. (1991). A theory of inferred causation. In Principles of Knowledge Representation and Reasoning: Proceedings of the 2nd International Conference, Ed. J. A. Allen, R. Fikes and E. Sandewall, pp. 441–52. San Mateo, CA: Morgan Kaufmann.Google ScholarGoogle Scholar
  27. Pratt, J. W. & Schlaifer, R. (1988). On the interpretation and observation of laws. J. Economet. 39, 23–52.Google ScholarGoogle ScholarCross RefCross Ref
  28. Robins, J. M. (1986). A new approach to causal inference in mortality studies with a sustained exposure period—applications to control of the healthy workers survivor effect. Math. Model. 7, 1393–512.Google ScholarGoogle ScholarCross RefCross Ref
  29. Robins, J. M. (1989). The analysis of randomized and non-randomized AIDS treatment trials using a new approach to causal inference in longitudinal studies. In Health Service Research Methodology: A Focus on AIDS, Ed. L. Sechrest, H. Freeman and A. Mulley, pp. 113–59. Washington, D.C.: NCHSR, U.S. Public Health Service.Google ScholarGoogle Scholar
  30. Robins, J. M., Blevins, D., Ritter, G. & Wulfsohn, M. (1992). G-estimation of the effect of prophylaxis therapy for pneumocystis carinii pneumonia on the survival of AIDS patients. Epidemiology 3, 319–36.Google ScholarGoogle ScholarCross RefCross Ref
  31. Rosenbaum, P. R. (1984). The consequences of adjustment for a concomitant variable that has been affected by the treatment. J. R. Statist. Soc. A 147, 656–66.Google ScholarGoogle ScholarCross RefCross Ref
  32. Rosenbaum, P. & Rubin, D. (1983). The central role of propensity score in observational studies for causal effects. Biometrika 70, 41–55.Google ScholarGoogle ScholarCross RefCross Ref
  33. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol. 66, 688–701.Google ScholarGoogle ScholarCross RefCross Ref
  34. Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. Ann. Statist. 7, 34–58.Google ScholarGoogle ScholarCross RefCross Ref
  35. Rubin, D. B. (1990). Neyman (1923) and causal inference in experiments and observational studies. Statist. Sci. 5, 472–80.Google ScholarGoogle ScholarCross RefCross Ref
  36. Simon, H. A. (1953). Causal ordering and identifiability. In Studies in Econometric Method, Ed. W. C. Hood and T. C. Hoopmans, Ch. 3. New York: John Wiley.Google ScholarGoogle Scholar
  37. Sobel, M. E. (1990). Effect analysis and causation in linear structural equation models. Psychometrika 55, 495–515.Google ScholarGoogle ScholarCross RefCross Ref
  38. Spiegelhalter, D. J., Lauritzen, S. L., Dawid, A. P. & Cowell, R. G. (1993). Bayesian analysis in expert systems (with Discussion). Statist. Sci. 8, 219–47.Google ScholarGoogle ScholarCross RefCross Ref
  39. Spirtes, P. (1995). Directed cyclic graphical representations of feedback models. In Uncertainty in Artificial Intelligence 11, Ed. P. Besnard and S. Hanks, pp. 491–98. San Mateo, CA: Morgan Kaufmann.Google ScholarGoogle Scholar
  40. Spirtes, P., Glymour, C. & Scheines, R. (1993). Causation, Prediction, and Search. New York: Springer-Verlag.Google ScholarGoogle ScholarCross RefCross Ref
  41. Strotz, R. H. & Wold, H. O. A. (1960). Recursive versus nonrecursive systems: An attempt at synthesis. Econometrica 28, 417–27.Google ScholarGoogle ScholarCross RefCross Ref
  42. Wainer, H. (1989). Eelworms, bullet holes, and Geraldine Ferraro: Some problems with statistical adjustment and some solutions. J. Educ. Statist. 14, 121–40.Google ScholarGoogle ScholarCross RefCross Ref
  43. Wermuth, N. (1992). On block-recursive regression equations (with Discussion). Brazilian J. Prob. Statist. 6, 1–56.Google ScholarGoogle Scholar
  44. Whittaker, J. (1990). Graphical Models in Applied Multivariate Statistics. Chichester: John Wiley.Google ScholarGoogle Scholar
  45. Wright, S. (1921). Correlation and causation. J. Agric. Res. 20, 557–85.Google ScholarGoogle Scholar
  46. Balke, A. & Pearl, J. (1995). Counterfactuals and policy analysis in structural models. In Uncertainty in Artificial Intelligence 11, Ed. P. Besnard and S. Hanks, pp. 11–8. San Francisco, CA: Morgan Kaufmann.Google ScholarGoogle Scholar
  47. Bishop, Y. M. M., Fienberg, S. E. & Holland, P. W. (1975). Discrete Multivariate Analysis: Theory and Practice. Cambridge, MA: MIT Press.Google ScholarGoogle Scholar
  48. Box, G. E. P. (1966). The use and abuse of regression. Technometrics 8, 625–9.Google ScholarGoogle ScholarCross RefCross Ref
  49. Cameron, E. & Pauling, L. (1976). Supplemental ascorbate in the supportive treatment of cancer: prolongation of survival times in terminal human cancer. Proc. Nat. Acad. Sci. (USA), 73, 3685–9.Google ScholarGoogle ScholarCross RefCross Ref
  50. Cochran, W. G. (1965). The planning of observational studies of human populations (with Discussion). J. R. Statist. Soc. A 128, 134–55.Google ScholarGoogle ScholarCross RefCross Ref
  51. Cochran, W. G. & Cox, G. M. (1957). Experimental Designs, 2nd ed. New York: Wiley.Google ScholarGoogle Scholar
  52. Cornfield, J., Haenszel, W., Hammond, E., Lilienfeld, A., Shimkin, M. & Wynder, E. (1959). Smoking and lung cancer: Recent evidence and a discussion of some questions. J. Nat. Cancer Inst. 22, 173–203.Google ScholarGoogle Scholar
  53. Dawid, A. P. (1984). Statistical theory. The prequential approach (with Discussion). J. R. Statist. Soc. A 147, 278–92.Google ScholarGoogle ScholarCross RefCross Ref
  54. Dawid, A. P. (1991). Fisherian inference in likelihood and prequential frames of reference (with Discussion). J. R. Statist. Soc. B 53, 79–109.Google ScholarGoogle Scholar
  55. Fairfield Smith, H. (1957). Interpretation of adjusted treatment means and regressions in analysis of covariance. Biometrics 13, 282–308.Google ScholarGoogle ScholarCross RefCross Ref
  56. Fisher, R. A. (1935). The Design of Experiments. Edinburgh: Oliver and Boyd.Google ScholarGoogle Scholar
  57. Freedman, D. (1991). Statistical models and shoe leather (with Discussion). In Sociological Methodology 1991, Ed. P. Marsden, Ch. 10. Washington, D.C.: American Sociological Association.Google ScholarGoogle Scholar
  58. Freedman, D. (1995). Some issues in the foundation of statistics (with Discussion). Foundat. Sci. 1, 19–83.Google ScholarGoogle ScholarCross RefCross Ref
  59. Goldberger, A. S. (1973). Structural equation methods in the social sciences. Econometrica 40, 979–1001.Google ScholarGoogle ScholarCross RefCross Ref
  60. Herzberg, A. M. & Cox, D. R. (1969). Recent work on design of experiments: A bibliography and a review. J. R. Statist. Soc. A 132, 29–67.Google ScholarGoogle ScholarCross RefCross Ref
  61. Hill, A. B. (1971). A Short Textbook of Medical Statistics, 10th ed. Place: Lippincott.Google ScholarGoogle Scholar
  62. Holland, P. (1986). Statistical and causal inference. J. Am. Statist. Assoc. 81, 945–70.Google ScholarGoogle ScholarCross RefCross Ref
  63. May, G., DeMets, D., Friedman, L., Furberg, C. & Passamani, E. (1981). The randomized clinical trial: Bias in analysis. Circulation 64, 669–73.Google ScholarGoogle ScholarCross RefCross Ref
  64. Moertel, C., Fleming, T., Creagan, E., Rubin, J., O’Connell, M. & 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 Engl. J. Med. 312, 137–41.Google ScholarGoogle ScholarCross RefCross Ref
  65. Neyman, J. (1923). On the application of probability theory to agricultural experiments. Essay on Principles, Section 9. Transl. (1990) in Statist. Sci. 5, 465–80.Google ScholarGoogle ScholarCross RefCross Ref
  66. Robins, J. M. (1987a). A graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods. J. Chronic Dis. 40, Suppl. 2, 139S–161S.Google ScholarGoogle ScholarCross RefCross Ref
  67. Robins, J. M. (1987b). Addendum to ‘A new approach to causal inference in mortality studies with sustained exposure periods—application to control of the healthy worker survivor effect’. Comp. Math. Applic. 14, 923–45.Google ScholarGoogle ScholarCross RefCross Ref
  68. Robins, J. M. (1993). Analytic methods for estimating HIV treatment and cofactor effects. In Methodological Issues of AIDS Mental Health Research, Ed. D. G. Ostrow and R. Kessler, pp. 213–90. New York: Plenum.Google ScholarGoogle Scholar
  69. Rosenbaum, P. R. (1984a). From association to causation in observational studies. J. Am. Statist. Assoc. 79, 41–8.Google ScholarGoogle ScholarCross RefCross Ref
  70. Rosenbaum, P. R. (1993). Hodges–Lehmann point estimates of treatment effect in observational studies. J. Am. Statist. Assoc. 88, 1250–3.Google ScholarGoogle ScholarCross RefCross Ref
  71. Rosenbaum, P. R. (1995). Observational Studies. New York: Springer-Verlag.Google ScholarGoogle ScholarCross RefCross Ref
  72. Rubin, D. B. (1976). Inference and missing data (with Discussion). Biometrika 63, 581–92.Google ScholarGoogle ScholarCross RefCross Ref
  73. Shafer, G. (1996). The Art of Causal Conjecture. Cambridge, MA: MIT Press.Google ScholarGoogle ScholarDigital LibraryDigital Library
  74. Smith, H. F. (1957). Interpretation of adjusted treatment means and regressions in analysis of covariates. Biometrics 13, 282–308.Google ScholarGoogle ScholarCross RefCross Ref
  75. Vovk, V. G. (1993). A logic of probability, with application to the foundations of statistics (with Discussion). J. R. Statist. Soc. B 55, 317–51.Google ScholarGoogle Scholar

Index Terms

  1. Causal Diagrams for Empirical Research (With Discussions)
              Index terms have been assigned to the content through auto-classification.

              Recommendations

              Comments

              Login options

              Check if you have access through your login credentials or your institution to get full access on this article.

              Sign in

              Full Access

              • Published in

                cover image ACM Books
                Probabilistic and Causal Inference: The Works of Judea Pearl
                February 2022
                946 pages
                ISBN:9781450395861
                DOI:10.1145/3501714

                Publisher

                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 4 March 2022

                Permissions

                Request permissions about this article.

                Request Permissions

                Check for updates

                Qualifiers

                • chapter

                Appears In

              PDF Format

              View or Download as a PDF file.

              PDF

              eReader

              View online with eReader.

              eReader