References
[1] Aickin, M. (2002). Causal Analysis in Biomedicine and Epidemiology. New York: Marcel Dekker.
[2] Anderson, J. (1938). The problem of causality. Australasian J. of Psychology and Philosophy, 16, 127-142.
[3] Andersson, S.A., Madigan, D. & Perlman, M.D. (1997). A characterization of Markov equivalence classes for acyclic digraphs. Ann. Statist., 25, 505-541.
[4] Aristotle (1928). Analytica Posteriora. In The Works of Aristotle, Ed. W.D. Ross, vol. 1. Oxford: Clarendon Press.
[5] Aristotle (1930). Physica. In The Works of Aristotle, Ed. W.D. Ross, vol. II. Oxford: Clarendon Press.
[6] Arjas, E. {& } Parner, J. (2004). Casual reasoning from longitudinal data (with discussion). Scand. J. Statist., 31, 171-201.
[7] Bentler, P. & Peeler, W. (1979). Models of female orgasm. Archives of Sexual Behavior, 8, 405-423.
[8] Berger, J.O. (1985). Statistical Decision Theory and Bayesian Analysis. New York: Springer.
[9] Blau, P. & Duncan, O. (1967). The American Occupational Structure. New York: Wiley.
[10] Bickel, P.J., Hammel, E.A. & O'Connell, J.W. (1977). Sex bias in graduate admissions: Data from Berkeley. In Statistics and Public Policy, Eds. W.B. Fairley and F. Mosteller. Reading, MA: Addison-Wesley.
[11] Carnap, R. (1950, 1962). Logical Foundations of Probability, Second Edition 1962. Chicago: University of Chicago Press.
[12] Copi, I.M. & Cohen, C. (1998). Logic, Tenth edition. Upper Saddle River, NJ: Prentice Hall.
[13] Cox, D.R. & Wermuth, N. (1993). Linear dependencies represented by chain graphs (with discussion). Statistical Science, 8, 204-218.
[14] Cox, D.R. & Wermuth, N. (1996). Multivariate Dependencies. London: Chapman and Hall.
[15] Cox, D.R. & Wermuth, N. (2004). Causality: a statistical view. Internat. Statist. Rev., 72, 285-305.
[16] Dawid, A.P. (1979). Conditional independence in statistical theory. J. Roy. Statist. Soc. B, 41, 1-31.
[17] Dawid, A.P. (2000). Causal inference without counterfactuals (with Discussion). J. Amer. Statist. Assoc., 95, 407-448.
[18] Dawid, A.P. (2002). Influence diagrams for causal modelling and inference. Internat. Statist. Rev., 70, 161-189.
[19] Edwards, D. (2000). Introduction to Graphical Modelling, Second Edition. New York: Springer.
[20] Eells, E. (1991). Probabilistic Causality. Cambridge: Cambridge University Press.
[21] Faliva, M. (1992). Recursiveness vs. interdependence in econometric models: a comprehensive analysis for the linear case. J. Ital. Statist. Soc., 1, 335-357.
[22] Faliva, M. & Zoia, M.G. (1994). Detecting and testing causality in linear econometric models. J. Ital. Statist. Soc., 3, 61-76.
[23] Fisher, R.A. (1959). Smoking: the Cancer Controversy. Edinburgh: Oliver and Boyd.
[24] Frangakis, C.E. & Rubin, D.B. (2002). Principal stratification in causal inference. Biometrics, 58, 21-29.
[25] Freedman, D. (1999). From association to causation: some remarks on the history of statistics. Statistical Science, 14, 243-258.
[26] Frosini, B.V. (1999). Conditioning, information, and frequentist properties. Statistica Applicata, 11, 165-184.
[27] Frosini, B.V. (2001). Metodi statistici. Roma: Carocci.
[28] Frosini, B.V. (2002). Le prove statistiche nel processo civile e nel processo penale. Milano: Giuffré.
[29] Frosini, B.V. (2004). On Neyman-Pearson theory: Information content of an experiment and a fancy paradox. Statistica, 64, 271-286.
[30] Frydenberg, M. (1990). The chain graph Markov property. Scand. J. Statist., 17, 333-353.
[31] Gail, M.H. (1986). Adjusting for covariates that have the same distribution in exposed and unexposed cohorts. In Modern Statistical Methods in Chronic Disease Epidemiology, Eds. S.H. Moolgavkar and R.L. Prentice, pp. 3-18. New York: Wiley.
[32] Götze, A. (1947). Old Babylonian Omen Texts. New Haven: Yale University Press.
[33] Goodman, L.A. (1973). The analysis of multidimensional contingency tables when some variables are posterior to others: A modified path analysis approach. Biometrika, 60, 179-192.
[34] Goodman, N. (1965-1983). Fact, Fiction, and Forecast, Fourth Edition. Cambridge, MA: Harvard University Press.
[35] Greenland, S., Pearl, J. & Robins, J.M. (1999). Confounding and collapsibility in causal inference. Statistical Science, 14, 29-46.
[36] Hempel, C.G. (1962). Deductive-Nomological vs Statistical Explanation. In Minnesota Studies in the Philosophy of Science, Eds. H. Feigl and G. Maxwell. Minneapolis: University of Minnesota Press.
[37] Hempel, C.G. (1965). Aspects of Scientific Explanation and Other Essays in the Philosophy of Science. New York: Free Press.
[38] Hempel, C.G. (1966). Philosophy of Natural Science. Englewood Cliffs, NJ: Prentice Hall.
[39] Hempel, C.G. & Oppenheim, P. (1948). Studies in the logic of explanation. Philosophy of Science, 15, 135-175.
[40] Hill, A.B. (1965). The environment and disease; association or causation. Proceedings of the Royal Society of Medicine, 58, 295-300.
[41] Holland, P.W. (1986). Statistics and causal inference. J. Amer. Statist. Assoc., 81, 945-970.
[42] Hume, D. (1739-1888). A Treatise of Human Nature. Oxford: Clarendon Press.
[43] Jeffrey, R.C. (1969). Statistical explanation vs. statistical inference. In Essays in Honor of Carl G. Hempel, Ed. N. Rescher N. Dordrecht: D. Reidel.
[44] Laplace, P.S. (1840). Essai philosophique sur les probabilit\'{es}, Septième edition. Bruxelles: SociétéBelge de Librairie.
[45] Lauritzen, S.L. (1996). Graphical Models. Oxford: Clarendon Press.
[46] Lauritzen, S.L. (2001). Causal inference from graphical models. In Complex Stochastic Systems, Ed. O.E. Barndorff-Nielsen, pp. 63-107. London: Chapman and Hall.
[47] Lauritzen, S.L. (2004). Discussion on causality. Scand. J. Statist., 31, 189-192.
[48] Lauritzen, S.L., Dawid, A.P., Larsen, B.N. & Leimer, H.-G. (1990). Independence properties of directed Markov fields. Networks, 20, 491-505.
[49] Lindley, D.V. (1979). Discussion of Dawid's paper. J. Roy. Statist. Soc. B, 41, 15-16.
[50] Lindley, D.V. (2002). Seeing and doing: the concept of causation (with discussion). Internat. Statist. Rev., 70, 191-214.
[51] Mackie, J.L. (1965). On causes and conditions. American Philosophical Quarterly, pp. 245-255 and 261-264.
[52] Mackie, J.L. (1974). The Cement of the Universe: A Study of Causation. Oxford: Oxford University Press.
[53] Maldonado, G. & Greenland, S. (2002). Estimating causal effects. Int. J. Epidemiology, 31, 422- 429.
[54] Mill, J.S. (1843). A System of Logic. London: J.V. Parker.
[55] Neyman, J. (1923). Justification of applications of the calculus of probabilities to the solutions of certain questions in agricultural experimentation (Polish, German summary). Polish Forest Agric. Journal, 10, 1-51. (English translation of excerpts in Statistical Science, (1990) 5, 465-472).
[56] Pearl, J. (1995). Causal diagrams for empirical research. Biometrika, 82, 669-710.
[57] Pearl, J. (1996). Causation, action, and counterfactuals. In Computational Learning and Probabilistic Reasoning, Ed. A. Gammerman. Chichester: Unicom-Wiley.
[58] Pearl, J. (2000). Causality. Models, Reasoning, and Inference. Cambridge: Cambridge University Press.
[59] Pearson, K. (1900). The Grammar of Science, Second Edition. London: Adam and Charles Black.
[60] Prentice, R.L. (1989). Surrogate endpoints in clinical trials: Definition and operational criteria. Statistics in Medicine, 8, 431-440.
[61] Reichenbach, H. (1956). The Direction of Time. Berkeley: University of California Press.
[62] Robins, J.M. (1986). A new approach to casual inference in mortality studies with sustained exposure-Application to control of the healthy worker survivor effect. Math. Modelling, 7, 1393-1512.
[63] Robins, J.M. (1997). Causal inference from complex longitudinal data. In Latent Variable Modelling with Applications to Causality, Ed. M. Berkane, pp. 69-117. New York: Springer.
[64] Robins, J.M. (1998). Structural nested failure time models. In: The Encyclopedia of Biostatistics, Eds. P. Armitage and T. Colton, pp. 4372-4389. Chichester: Wiley.
[65] Rosenbaum, P.R. (1984). From association to causation in observational studies: The role of tests of strongly ignorable treatment assignment. J. Amer. Statist. Assoc., 79, 41-48.
[66] Rosenbaum, P.R. & Rubin, D.B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41-55.
[67] Rothman, K.J. (1976). Causes, American J. of Epidemiology. 104, 587-592.
[68] Rothman, K.J. (Ed.) (1988). Causal Inference. Chestnut Hill, MA: Epidemiology Resources.
[69] Rothman, K.J. & Greenland, S. (1998). Modern Epidemiology. Philadelphia: Lippincott-Raven.
[70] Rubin, D.B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. J. of Educational Psychology, 66, 688-701.
[71] Rubin, D.B. (1977). Assignment to treatment group on the basis of a covariate. J. of Educational Statistics, 2, 1-26.
[72] Rubin, D.B. (1978). Bayesian inference for causal effects: the role of randomization. Ann. Statist., 6, 34-58.
[73] Rubin, D.B. (1980). Discussion of Basu's paper. J. Amer. Statist. Assoc., 75, 591-593.
[74] Rubin, D.B. (2000). Comment on Dawid's paper. J. Amer. Statist. Assoc., 95, 435-438.
[75] Rubin, D.B. (2004). Direct and indirect causal effects via potential outcomes. Scand. J. Statist. 31, 161-170.
[76] Russell, B. (1913). On the notion of cause. Proceedings of the Aristotelian Society, 13, 1-26.
[77] Salmon, W.C. (1965). The status of prior probabilities in statistical explanation. Philosophy of Science, 32, 137-146.
[78] Salmon, W.C. (1978). Why ask ''Why''?: An inquiry concerning scientific explanation. Proceedings and Addresses of the American Philosophical Association, 5(6), 683-705
[79] Salmon, W.C. (1984). Scientific Explanation and the Causal Structure of the World. Princeton: Princeton University Press.
[80] Scriven, M. (1959). Explanation and prediction in evolutionary theory. Science, 130, 477-482.
[81] Simpson, C. (1951). The interpretation of interaction in contingency tables. J. Roy. Statist. Soc. B, 13, 238-241.
[82] Speed, T.P. (1990). Introductory remarks on Neyman (1923). Statistical Science, 5, 463-464.
[83] Spirtes, P., Glymour, C. & Scheines, R. (2000). Causation, Prediction, and Search. Cambridge, MA: The MIT Press.
[84] Suppes, P. (1970). A Probabilistic Theory of Causality. Amsterdam: North Holland.
[85] van der Laan, M.J. {& } Robins, J.M. (2002). Unified Methods for Censored Longitudinal Data and Causality. New York: Springer.
[86] Verma, T. & Pearl, J. (1990). Equivalence and synthesis of causal models. In Uncertainty in Artificial Intelligence, Proceedings of the Sixth Conference, pp. 220-227. San Francisco: Morgan Kaufman.
[87] Wermuth, N. & Lauritzen, S.L. (1983). Graphical and recursive models for contingency tables. Biometrika, 70, 537-552.
[88] Wermuth, N. & Lauritzen, S.L. (1990). On substantive research hypotheses, conditional independence graphs and graphical chain models (with discussion). J. R. Statist. Soc. B, 52, 21-72.
[89] Wright, S. (1921). Correlation and causation. J. Agric. Res., 20, 557-585.
[90] Wright, S. (1934). The method of path coefficients. Ann. Math. Statist., 5, 161-215.
[91] Yule, G.U. (1903). Notes on the theory of association of attributes in Statistics. Biometrika, 2, 121-134.
[92] Zeisel, H. & Kaye, D. (1997). Prove it with Figures. New York: Springer.