The structural theory of causation

Causality in the Sciences

Volumn , Issue , 2011, Pages

  Pearl, Judea ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Causal effects; Confounding; Counterfactuals; Graphical methods; Potential outcome; Probabilistic causation; Structural equation models

Indexed keywords

EID: 79959758768     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1093/acprof:oso/9780199574131.003.0033     Document Type: Chapter

References (74)

1. Angrist, J.D., Imbens, G.W., and Rubin, D.B. (1996). Identification of causal effects using instrumental variables (with comments). Journal of the American Statistical Association, 91(434), 444-472.
2. Arah, O.A. (2008). The role of causal reasoning in understanding Simpson's paradox, Lord's paradox, and the suppression effect: Covariate selection in the analysis of observational studies. Emerging Themes in Epidemiology, 4, doi:10.1186/1742-7622-5-5. Online at http://www.ete-online.com/content/5/1/5.
3. Avin, C., Shpitser, I., and Pearl, J. (2005). Identifiability of path-specific effects. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence IJCAI-05, Edinburgh, UK, pp. 357-363. Morgan-Kaufmann Publishers.
4. Balke, A. and Pearl, J. (1994a). Counterfactual probabilities: Computational methods, bounds, and applications. In Uncertainty in Artificial Intelligence 10 (ed. R. L. de Man- taras and D. Poole), pp. 46-54. Morgan Kaufmann, San Mateo, CA.
5. Balke, A. and Pearl, J. (1994b). Probabilistic evaluation of counterfactual queries. In Proceedings of the Twelfth National Conference on Artificial Intelligence, Volume I, pp. 230-237. MIT Press, Menlo Park, CA.
6. Balke, A. and Pearl, J. (1995). Counterfactuals and policy analysis in structural models. In Uncertainty in Artificial Intelligence 11 (ed. P. Besnard and S. Hanks), pp. 11-18. Morgan Kaufmann, San Francisco.
7. Brent, R. and Lok, L. (2005). A fishing buddy for hypothesis generators. Science, 308(5721), 523-529.
8. Cartwright, N. (1983). How the Laws of Physics Lie. Clarendon Press, Oxford.
9. Chalak, K. and White, H. (2006, July). An extended class of instrumental variables for the estimation of causal effects. Technical Report Discussion Paper, UCSD, Department of Economics.
10. Cole, S.R. and Hernán, M.A. (2002). Fallibility in estimating direct effects. International Journal of Epidemiology, 31(1), 163-165.
11. Collins, J., Hall, N., and Paul, L.A. (eds.) (2004). Causation and Counterfactuals. MIT Press, Cambridge, MA.
12. Cox, D.R. (1958). The Planning of Experiments. John Wiley and Sons, New York.
13. Cox, D.R. and Wermuth, N. (2004). Causality: A statistical view. International Statistical Review, 72(3), 285-305.
14. Dawid, A.P. (1979). Conditional independence in statistical theory. Journal of the Royal Statistical Society, Series B, 41(1), 1-31.
15. Dawid, A.P. (2000). Causal inference without counterfactuals (with comments and rejoinder). Journal of the American Statistical Association, 95(450), 407-448.
16. Dawid, A.P. (2002). Influence diagrams for causal modelling and inference. International Statistical Review, 70, 161-189.
17. Duncan, O.D. (1975). Introduction to Structural Equation Models. Academic Press, New York.
18. Eells, E. (1991). Probabilistic Causality. Cambridge University Press, Cambridge, MA.
19. Glymour, M.M. and Greenland, S. (2008). Causal diagrams. In Modern Epidemiology (3rd edn) (ed. K. Rothman, S. Greenland, and T. Lash), pp. 183-209. Lippincott Williams & Wilkins, Philadelphia, PA.
20. Good, I.J. (1961). A causal calculus (I). British Journal for the Philosophy of Science, 11, 305-318.
21. Greenland, S. and Brumback, B. (2002). An overview of relations among causal modelling methods. International Journal of Epidemiology, 31, 1030-1037.
22. Greenland, S., Pearl, J., and Robins, J.M (1999). Causal diagrams for epidemiologic research. Epidemiology, 10(1), 37-48.
23. Haavelmo, T. (1943). The statistical implications of a system of simultaneous equations. Econometrica, 11, 1-12. Reprinted in D.F. Hendry and M.S. Morgan (Eds.), The Foundations of Econometric Analysis, Cambridge University Press, 477-490, 1995.
24. Heckman, J.J. (2008). Econometric causality. International Statistical Review, 76(1), 1-27.
25. Hitchcock, C. (2001). Book reviews: Causality: Models, Reasoning and Inference. The Philosophical Review, 110(4), 639-641.
26. Hitchcock, C.R. (2003). Probabilistic causation. In Stanford Encyclopedia of Philosophy (Winter 2003 Edition) (ed. E. Zalta). URL = http://plato.stanford.edu/entries/causation-probabilistic/, Stanford, CA.
27. Holland, P.W. (1988). Causal inference, path analysis, and recursive structural equations models. In Sociological Methodology (ed. C. Clogg), pp. 449-484. American Sociological Association, Washington, D.C.
28. Lauritzen, S.L. (1996). Graphical Models. Clarendon Press, Oxford.
29. Lauritzen, S.L. (2001). Causal inference from graphical models. In Complex Stochastic Systems (ed. D. Cox and C. Kluppelberg), pp. 63-107. Chapman and Hall/CRC Press, Boca Raton, FL.
30. Lewis, D. (1986). Philosophical Papers, Volume II. Oxford University Press, New York.
31. Lindley, D.V. (2002). Seeing and doing: The concept of causation. International Statistical Review, 70, 191-214.
32. Meek, C. and Glymour, C.N. (1994). Conditioning and intervening. British Journal of Philosophy Science, 45, 1001-1021.
33. Morgan, S.L. and Winship, C. (2007). Counterfactuals and Causal Inference:Methods and Principles for Social Research (Analytical Methods for Social Research). Cambridge University Press, New York.
34. Neyman, J. (1923). On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Statistical Science, 5(4), 465-480.
35. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, CA.
36. Pearl, J. (1993a). Comment: Graphical models, causality, and intervention. Statistical Science, 8(3), 266-269.
37. Pearl, J. (1993b). Mediating instrumental variables. Technical Report TR- 210, http://ftp.cs.ucla.edu/pub/stat_ser/R210.pdf, Department of Computer Science, University of California, Los Angeles.
38. Pearl, J. (1995). Causal diagrams for empirical research. Biometrika, 82(4), 669-710.
39. Pearl, J. (1998). Graphs, causality, and structural equation models. Sociological Methods and Research, 27(2), 226-284.
40. Pearl, J. (2000a). Causality: Models, Reasoning, and Inference. Second edn. 2009 Cambridge University Press, New York.
41. Pearl, J. (2000b). Comment on A.P. Dawid's, Causal inference without counterfactuals. Journal of the American Statistical Association, 95(450), 428-431.
42. Pearl, J. (2001). Direct and indirect effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pp. 411-420. Morgan Kaufmann, San Francisco, CA.
43. Pearl, J. (2003a). Reply to Woodward. Economics and Philosophy, 19, 341-344.
44. Pearl, J. (2003b, December). Statistics and causal inference: A review. Test Journal, 12(2), 281-345.
45. Pearl, J. (2005). Direct and indirect effects. In Proceedings of the American Statistical Association, Joint Statistical Meetings, pp. 1572-1581. MIRA Digital Publishing, Minn., MN.
46. Pearl, J. (2009a). Causality: Models, Reasoning, and Inference, Second edn. Cambridge University Press, New York.
47. Pearl, J. (2009b). Letter to the editor: Remarks on the method of propensity scores. Statistics in Medicine, 28, 1415-1416. http://ftp.cs.ucla.edu/pub/stat_ser/r345-sim.pdf.
48. Pearl, J. (2009c). Myth, confusion, and science in causal analysis. Technical Report R-348, University of California, Los Angeles, CA. http://ftp.cs.ucla.edu/pub/stat_ser/r348.pdf.
49. Pearl, J. and Robins, J.M. (1995). Probabilistic evaluation of sequential plans from causal models with hidden variables. In Uncertainty in Artificial Intelligence 11 (ed. P. Besnard and S. Hanks), pp. 444-453. Morgan Kaufmann, San Francisco.
50. Petersen, M.L., Sinisi, S.E., and van der Laan, M.J. (2006). Estimation of direct causal effects. Epidemiology, 17(3), 276-284.
51. 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. Mathematical Modeling, 7, 1393-1512.
52. Robins, J.M. (1999). Testing and estimation of directed effects by reparameteriz- ing directed acyclic with structural nested models. In Computation, Causation, and Discovery (ed. C. Glymour and G. Cooper), pp. 349-405. AAAI/MIT Press, Cambridge, MA.
53. Robins, J.M. and Greenland, S. (1991). Estimability and estimation of expected years of life lost due to a hazardous exposure. Statistics in Medicine, 10, 79-93.
54. Rosenbaum, P.R. (2002). Observational Studies, Second edn. Springer-Verlag, New York.
55. Rosenbaum, P. and Rubin, D. (1983). The central role of propensity score in observational studies for causal effects. Biometrika, 70, 41-55.
56. Rothman, K.J. (1976). Causes. American Journal of Epidemiology, 104, 587-592.
57. Rubin, D.B. (1974). Estimating causal effects of treatments in randomized and non- randomized studies. Journal of Educational Psychology, 66, 688-701.
58. Rubin, D.B. (2004). Direct and indirect causal effects via potential outcomes. Scandinavian Journal of Statistics, 31, 161-170.
59. Rubin, D.B. (2009). Author's reply: Should observational studies be designed to allow lack of balance in covariate distributions across treatment group? Statistics in Medicine, 28, 1420-1423.
60. Shpitser, I. and Pearl, J. (2006a). Identification of conditional interventional distributions. In Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (ed. R. Dechter and T. Richardson), pp. 437-444. AUAI Press, Corvallis, OR.
61. Shpitser, I. and Pearl, J. (2006b). Identification of joint interventional distributions in recursive semi-Markovian causal models. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pp. 1219-1226. AAAI Press, Menlo Park, CA.
62. Shpitser, I. and Pearl, J. (2007). What counterfactuals can be tested. In Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence, pp. 352-359. AUAI Press, Corvallis, OR. Also, Journal of Machine Learning Research, 9: 1941-1979, 2008.
63. Shpitser, I. and Pearl, J. (2009). Effects of treatment on the treated:Identification and generalization. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 514-521. AUAI Press, Corvallis, OR.
64. Shrier, I. (2009). Letter to the editor: Propensity scores. Statistics in Medicine, 28, 1317-1318. See also Pearl 2009 #http://ftp.cs.ucla.edu/pub/stat_ser/r348.pdf#.
65. Simon, H.A. and Rescher, N. (1966). Cause and counterfactual. Philosophy and Science, 33, 323-340.
66. Sobel, M.E. (1998). Causal inference in statistical models of the process of socioeco- nomic achievement. Sociological Methods & Research, 27(2), 318-348.
67. Spirtes, P., Glymour, C.N., and Scheines, R. (2000). Causation, Prediction, and Search, second edn. MIT Press, Cambridge, MA.
68. Suppes, P. (1970). A Probabilistic Theory of Causality. North-Holland, Amsterdam.
69. Tian, J., Paz, A., and Pearl, J. (1998). Finding minimal separating sets. Technical Report R-254, University of California, Los Angeles, CA.
70. Tian, J. and Pearl, J. (2000). Probabilities of causation: Bounds and identification. Annals of Mathematics and Artificial Intelligence, 28, 287-313.
71. Tian, J. and Pearl, J. (2002). A general identification condition for causal effects. In Proceedings of the Eighteenth National Conference on Artificial Intelligence, pp. 567-573. AAAI Press/The MIT Press, Menlo Park, CA.
72. Williamson, J. (2010). Probabilistic theories of causality. In The Oxford Handbook of Causation (ed. H. Beebee, C. Hitchcock, and P. Peter). Oxford University Press, New York.
73. Woodward, J. (2003). Making Things Happen. Oxford University Press, New York, NY.
74. Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20, 557-585.