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Supporting the Rationale of Variation

Part of the Methodos Series book series (METH, volume 5)

This chapter supports the rationale of variation by showing how the notion of variation is involved or consistent with a number of philosophical accounts: the mechanist and counterfactual accounts, agency and manipulability theories, epis-temic and singularist accounts.

Keywords

Mechanist account counterfactual account agency theory manipula-bility theory epistemic causality causality in single instances W. Salmon P. Dowe D. Lewis P. Menzies H. Price J. Woodward D. Hausman C. J. Ducasse J. Williamson 

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Bibliography

  1. Abbott, A. (1998). The causal devolution. Sociological Methods and Research, 27(2), 148–181.Google Scholar
  2. Adams, P., Hurd, M., McFadden, D., Merrill, A., Ribeiro, T. (2003). Healthy, wealthy, and wise? Tests for direct causal paths between health and socioeconomic status. Journal of Econometrics, 112, 3–56. With discussion.Google Scholar
  3. Agresti, A. (1996). An introduction to categorical data analysis. New York: Wiley.Google Scholar
  4. Aish-Van Vaerenbergh, A. -M. (1994). Modèles statistiques et inférences causales: analyse des structures de covariances avec LISREL. In R. Franck (Ed.), Faut-il chercher aux causes uneraison? (pp. 106–130). Paris: Vrin.Google Scholar
  5. Aish-Van Vaerenbergh, A. -M. (2002). Explanatory models in suicide research: explaining relationships. In R. Franck (Ed.), The explanatory power of models (pp. 51–66). Dordrecht: Kluwer.Google Scholar
  6. Aristotle (1928). Metaphysics. Translated by W. D. Ross, The works of Aristotle translated into English, vol. 8, Second edition. Oxford: Clarendon.Google Scholar
  7. Atmanspacher, H., Bishop, R. (2002) (Ed.). Between chance and choice: interdisciplinary perspectives on determinism. Thoverton: Imprint Academic.Google Scholar
  8. Bayarri, S., Berger, J. O. (2000). P-values for composite null models. Journal of the American Statistical Association, 95(452), 1127–1142. With discussion.Google Scholar
  9. Beck, W., van der Maeson, L., Walker, A. (2001). Introduction: who and what is the European Union for? In W. Beck, L. van der Maeson, G. Thomése, A. Walker (Ed.), Social quality: a vision for Europe (pp. 1–18). The Hague: Kluwer Law International.Google Scholar
  10. Berger, J. O., Boukai, B., Wang, Y. (1997). Unified frequentist and Bayesian testing of a precise hypothesis. Statistical Science, 12(3), 133–160.Google Scholar
  11. Berger, J. O., Delampady, M. (1987). Testing precise hypotheses. Statistical Science, 3(2), 317–335.Google Scholar
  12. Berger, J. O.,Guglielmi, A. (2001). Bayesian and conditional frequentist testing of a parametric model versus nonparametric alternatives. Journal of the American Statistical Association, 96(453), 174–184.Google Scholar
  13. Berger, J. O., Guglielmi, A. (2001). Bayesian and conditional frequentist testing of a parametric model versus nonparametric alternatives. Journal of the American Statistical Association, 96(453), 174–184.Google Scholar
  14. Berkovitz, J. (2002). On causal inference in determinism and indeterminism. In H. Atmanspacher, R. Bishop (Ed.), Between chance and choice: interdisciplinary perspectives on determinism (pp. 237–278). Thoverton: Imprint Academic.Google Scholar
  15. Bernardo, J. M. (1980). A Bayesian analysis of classical hypothesis testing. In J. Bernardo, M. H. DeGroot, D. V. Lindley, A. F. M. Smith (Ed.), Bayesian statistics (pp. 605–647). Valencia: Valencia University Press. With discussion.Google Scholar
  16. Bessie, J. D. (1993). On the strength of a causal chain. Pacific Philosophical Quarterly, 74(1), 11–36.Google Scholar
  17. Blalock, H. M. (1964). Causal inferences in nonexperimental research. Chapel Hill: University of North Carolina Press.Google Scholar
  18. Blalock, H. M. (1968a). The measurement problem: a gap between the languages of theory and research. In H. M. Jr. Blalock, A. Blalock (Ed.), Methodology in social research (pp. 5–27). New York: McGraw Hill.Google Scholar
  19. Blalock, H. M. (1968b). Theory building and causal inferences. In H. M. Jr. Blalock, A. Blalock (Ed.), Methodology in social research (pp. 155–198). New York: McGraw Hill.Google Scholar
  20. Boudon, R. (1967). L'analyse mathe‘matique des faits sociaux. Paris: Plon.Google Scholar
  21. Boudon, R., Lazarfeld, P. (1966) (Ed.). L'analyse empirique de la causalite’;. Paris: Mouton & Co.Google Scholar
  22. Bovens, L., Hartmann, S. (2003). Bayesian epistemology. Oxford: Clarendon Press.Google Scholar
  23. Bunge, M. A. (1979a). Causality and modern science. New York: Dover. Third revised edtion.Google Scholar
  24. Bunge, M. A. (1979b). A world of systems. Dordrecht: Reidel Publishing Company.Google Scholar
  25. Bunge, M. A. (1996). Finding philosophy in social science. New Haven, CT: Yale University Press.Google Scholar
  26. Bunge, M. A. (1998). Social science under debate: a philosophical perspective. Toronto: University of Toronto Press.Google Scholar
  27. Bunge, M. A. (2004). How does it work? The search for explanatory mechanisms. Philosophy of the Social Sciences, 34(2), 182–210.Google Scholar
  28. Caldwell, J. C. (1979). Education as a factor in mortality decline: an examination of Nigerian data. Population Studies, 33(3), 395–413.Google Scholar
  29. Campbell, D. T., Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Chicago: Rand McNally.Google Scholar
  30. Carnap, R. (1951). The logical foundations of probability. Chicago: University of Chicago Press. Second edition.Google Scholar
  31. Carroll, J. W. (2003). Laws of nature. In E. N. Zalta (Ed.), The Stanford encyclopaedia of philosophy. (Fall 2003 edition). http://plato.stanford.edu/archives/fall2003/entries/laws-of-nature/. Accessed 12 March 2008.
  32. Cartwright, N. (1979). Causal laws and effective strategies. Noûs, 13(4), 419–437.Google Scholar
  33. Cartwright, N. (1989). Nature's capacities and their measurements. Oxford: Clarendon Press.Google Scholar
  34. Cartwright, N. (1995). Précis of ‘Nature's capacities and their measurement’. Philosophy and Phenomenological Research, 55(1), 153–156.Google Scholar
  35. Cartwright, N. (1997). What is a causal structure? In V. McKim, S. P. Turner (Ed.), Causality in crisis? Statistical methods and the search for causal knowledge in the social sciences (pp. 343–357). Indiana: University of Notre Dame Press.Google Scholar
  36. Cartwright, N. (1999). Causal diversity and Markov condition. Synthese, 121, 3–27.Google Scholar
  37. Cartwright, N. (2000). Measuring causes: invariance, modularity and the causal Markov condition. Measurement in physics and economics discussion paper series. Centre for Philosophy of Natural and Social Science, 10/00.Google Scholar
  38. Cartwright, N. (2002). Against modularity, the causal Markov condition, and any link between the two: comments on Hausman and Woodward. British Journal for the Philosophy of Science, 53(3), 411–453.Google Scholar
  39. Cartwright, N. (2007a). Hunting causes and using them: approaches in philosophy and economics. Cambridge: Cambridge University Press.Google Scholar
  40. Cartwright, N. (2007b). Are RCTs the gold standard? Bio Societies, 2, 11–20.Google Scholar
  41. Chakravartty, A. (2005). Causal realism: events and processes. Erkenntnis, 63, 7–31.Google Scholar
  42. Cohen, J. L. (1989). An introduction to the philosophy of induction and probability. Oxford: Clarendon Press.Google Scholar
  43. Collingwood, R. (1940/1998). An essay on metaphysics. Oxford: Clarendon Press.Google Scholar
  44. Cook, T. D., Campbell D. T. (1979). Quasi-experimentation. Design and analysis issues for field settings. Chicago: Rand MacNally.Google Scholar
  45. Cook, T. D., Campbell D. T. (1986). The causal assumptions of quasi-experimental practice. Synthese, 68, 141–180.Google Scholar
  46. Corfield, D., Williamson J. (2001) (Ed.). Foundations of Bayesianism. Dordrecht: Kluwer.Google Scholar
  47. Courgeau, D. (1994). Du groupe à l'individu: l'exemple des comportements migratoires. Population, 1, 7–26.Google Scholar
  48. Courgeau, D. (2002). Vers une analyse biographique multiniveaux. In M. Christine (Ed.), Actes des journeés de méthodologie statistique (pp. 375–394), INSEE méthodes 101.Google Scholar
  49. Courgeau, D. (2003) (Ed.). Methodology and epistemology of multilevel analysis. Approaches from different social sciences. Dordrecht: Kluwer.Google Scholar
  50. Courgeau, D. (2004a). Du groupe àl'individu: synthése multiniveau. Paris: Editions de l'INED.Google Scholar
  51. Courgeau, D. (2004b). Probabilité, démographie et sciences sociales. Mathématiques et sciences humaines/Mathematics and Social Sciences, 167(3), 27–50.Google Scholar
  52. Courgeau, D. (2007a). Multilevel synthesis: from the group to the individual. Dordrecht: Springer.Google Scholar
  53. Courgeau, D. (2007b). Inférence statistique, échangeabilité et approche multiniveau. Mathématique et sciences humaines/Mathematics and Social Sciences, 179(3), 5é19.Google Scholar
  54. Cox, D. R. (1992). Causality: some statistical aspects. Journal of the Royal Statistical Society, 155(2), 291é301.Google Scholar
  55. Cox, R. T. (1946). Probability, frequency, and reasonable expectation. American Journal of Physics, 14(1), 1–13.Google Scholar
  56. Cox, D. T. (2000). Causal inference without counterfactuals: commentary. Journal of the American Statistical Association, 95, 424–425.Google Scholar
  57. Dawid, A. P. (1982). The well-calibrated Bayesian. Journal of the American Statistical Association, 77(379), 605–610.Google Scholar
  58. Dawid, A. P. (2000). Causal inference without counterfactuals. Journal of the American Statistical Association, 95, 407–427.Google Scholar
  59. Dawid, A. P. (2002a). Influence diagrams for causal modelling and inference. International Statistical Review, 70, 161–189. Corrigenda, ibidem, 437.Google Scholar
  60. Dawid, A. P. (2002b). Commentary: counterfactuals: help or hindrance? International Journal of Epidemiology, 31, 429–430.Google Scholar
  61. Dawid, A. P. (2007). Counterfactuals, hypotheticals and potential responses: a philosophical examination of statistical causality. In F. Russo, J. Williamson (Ed.), Causality and probability in the sciences (pp. 503–532). London: College Publications.Google Scholar
  62. de Finetti, B. (1937). Foresight. Its logical laws, its subjective sources. In H. E. Kyburg, H. E. Smokler (Ed.), Studies in subjective probability (pp. 194–207). Huntington, New York: Wiley.Google Scholar
  63. de Finetti, B. (1993). Probabilitàe induzione. Induction and probability. Edited by D. Montanari, D. Cocchi. Bologna: CLUEB.Google Scholar
  64. Dowe, P. (1992). Wesley Salmon's process theory of causality and the conserved quantity theory. Philosophy of Science, 59, 195–216.Google Scholar
  65. Drèze, J. H., Mouchart, M. (1990). Tales of testing Bayesians. In R. A. L. Carter, J. Dutta, A. Ullah (Ed.), Contributions to econometric theory and applications - Essays in honour of A. L. Nagar (pp. 345–366). New York: Springer.Google Scholar
  66. Droesbeke, J.-J., Fine, J., Saporta, G. (2002). M èthodes bayèsiennes en statistique. Paris: Technip.Google Scholar
  67. Ducasse, J. C. (1926). On the nature and observability of the causal relation. Journal of Philosophy, 23, 57–68.Google Scholar
  68. Duchêne, J., Wunsch, G. (1985). From theory to statistical model. In IUSSP, International Population Conference (pp. 209–224), Volume 2. Liège: Ordina.Google Scholar
  69. Duchêne, J., Wunsch, G. (1989). Conceptual frameworks and causal modelling. In L. Ruzicka, G. Wunsch, P. Kane (Ed.), Differential mortality. Methodological issues and biosocial factors (pp. 21–35). Oxford: Clarendon.Google Scholar
  70. Duchêne, J., Wunsch, G. (1989) (Ed.). L'explication en sciences sociales: la recherche des causesen dèmographie. Chaire Quetelet 1987. Louvain-la-Neuve: CIACO.Google Scholar
  71. Duchêne, J., Wunsch, G. (2006). Causalitè et modèles causaux. In G. Caselli, J. Vallin, G. Wunsch (Ed.), D èmographie: analyse et synthèse (pp. 315–334), Volume VIII, Observation, mèthodes auxiliaires, enseignement et recherche. Paris: Editions de l'Institut National d'Etudes Dèmographiques.Google Scholar
  72. Duncan, O. D. (1975). Introduction to structural equation models. New York: Academic Press.Google Scholar
  73. Dupré, J., Cartwright, N. (1988). Probability and causality: why Hume and indeterminism don't mix. Noûs, 22(4), 521–536.Google Scholar
  74. Durkheim, E. (1895/1912). Les règles de la méthode sociologique. Paris: Libraire Félix Arcan. Sixth edition.Google Scholar
  75. Durkheim, E. (1897/1960). Le suicide. Paris: Presses Universitaires de France.Google Scholar
  76. Edwards, A. W. F. (1972). Likelihood. An account of the statistical concept of likelihood and its application to scientific inference. Cambridge: Cambridge University Press.Google Scholar
  77. Edwards, A. W. F. (1997). What did Fisher mean by ‘inverse probability’ in 1912–1922? Statistical Science, 12(3), 177–184.Google Scholar
  78. Eells, E. (1991). Probabilistic causality. Cambridge: Cambridge University Press.Google Scholar
  79. Eells, E., Sober, E. (1983). Probabilistic causality and the question of transitivity. Philosophy of Science, 50, 35–57.Google Scholar
  80. Ehring, D. (1984). Probabilistic causality and pre-emption. British Journal for the Philosophy of Science, 35, 55–57.Google Scholar
  81. Ellett, F. S., Ericson, D. P. (1983). The logic of causal methods in social science. Synthese, 57, 67–82.Google Scholar
  82. Ellett, F. S., Ericson, D. P. (1984). Probabilistic causal systems and the conditional probability approach to causal analysis. Quality and Quantity, 18, 247–259.Google Scholar
  83. Ellett, F. S., Ericson, D. P. (1986a). Correlation, partial correlation, and causation. Synthese, 67, 157–173.Google Scholar
  84. Ellett, F. S., Ericson, D. P. (1986b). An analysis of probabilistic causation in dichotomous structures. Synthese, 67, 175–193.Google Scholar
  85. Ellett, F. S., Ericson, D. P. (1989). Causal modelling and theories of causation. In J. Duchêne, G. Wunsch (Ed.), L'explication en sciences sociales: la recherche des causes en dèmographie (pp. 397–424). Chaire Quetelet 1987. Louvain-la-Neuve: CIACO.Google Scholar
  86. Engle, R. F., Hendry, D. F., Richard, J. -F. (1983). Exogeneity. Econometrica, 51(2), 277–304.Google Scholar
  87. Fetzer, J. (1988) (Ed.), Probability and causality. Dordrecht: Reidel.Google Scholar
  88. Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, 222, 309–368.Google Scholar
  89. Fisher, R. A. (1925). Statistical methods for research workers. London: Oliver and Boyd. http://psychclassics.yorku.ca/Fisher/Methods/. Accessed 14 March 2008.
  90. Fisher, R. A. (1930). Inverse probability. Proceeding of the Cambridge Philosophical Society, 26, 528–535.Google Scholar
  91. Fisher, R. A. (1935). The logic of inductive inference. Journal of the Royal Statistical Society, 98(1), 39–82.Google Scholar
  92. Florens, J. -P., Mouchart, M. (1989). Bayesian specification tests. In B. Cornet, H. Tulkens (Ed.), Contributions in operations research and economics (pp. 467–490). Cambridge: MIT Press.Google Scholar
  93. Florens, J. -P., Mouchart, M. (1993). Bayesian testing and testing Bayesians. In G. S. Maddala, C. R. Rao (Ed.), Hand-book of statistics (pp. 303–334). Amsterdam: North Holland.Google Scholar
  94. Franck, R. (1994) (Ed.). Faut-il chercher aux causes une raison? Paris: Vrin.Google Scholar
  95. Franck, R. (1995). MosaÏques, machines, organismes et sociètès. Revue Philosophique de Louvain, 93(1–2), 67–81.Google Scholar
  96. Franck, R. (2002) (Ed.). The explanatory power of models. Dordrecht: Kluwer.Google Scholar
  97. Franck, R. (2003). Causal analysis, systems analysis, and multilevel analysis: philosophy and epistemology. In D. Courgeau (Ed.), Methodology and epistemology of multilevel analysis. Approaches from different social sciences (pp. 175–198). Dordrecht: Kluwer.Google Scholar
  98. Freedman, D. A. (1999). From association to causation: some remarks on the history of statistics. Statistical Science, 14(3), 243–258.Google Scholar
  99. Freedman, D. A. (2004a). Statistical models for causation. Technical Report 651. http://www.stat.berkeley.edu/census/651.pdf. Accessed 14 March 2008.
  100. Freedman, D. A. (2004b). On specifying graphical models for causation, and the identification problem. Evaluation Review, 26, 267–93.Google Scholar
  101. Freedman, D. A. (2005). Statistical models. Theory and practice. Cambridge: Cambridge University Press.Google Scholar
  102. Freedman, D., Pisani, R., Purves, R. (1998). Statistics. New York: W.W. Norton. First edition.Google Scholar
  103. Fried, H. O, Schmidt, S. S., Lovell, K. (1993) (Ed.). The measurement of productive efficiency. New York: Oxford University Press.Google Scholar
  104. Galavotti, M. C., Suppes, P., Costantini, D. (2001) (Ed.), Stochastic causality. Stanford: CSLI.Google Scholar
  105. Gasking, D. (1955). Causation and recipes. Mind, 64, 479–487.Google Scholar
  106. Gérard, H. (1989). Théories et théorisation. In J. Duchêne, G. Wunsch (Ed.), L'explication en sciences sociales. La recherche des causes en démographie (pp. 267–281). Chaire Quetelet 1987. Louvain-la-Neuve: CIACO.Google Scholar
  107. Gérard, H. (2006). De la théorisation en démographie. In G. Caselli, J. Vallin, G. Wun-sch (Ed.), D émographie: analyse et synthèse (pp. 291–314). Volume VIII, Observation, méthodes auxiliaires, enseignement et recherche, Paris: Editions de l'Institut National d'Etudes Démographiques.Google Scholar
  108. Giere, R. (1984). Causal models with frequency dependence. Journal of Philosophy, 81, 384–391.Google Scholar
  109. Giere, R. (1999), Science without laws. Chicago: University of Chicago Press.Google Scholar
  110. Gillies, D. (2000). Philosophical theories of probability. London: Routledge.Google Scholar
  111. Glymour, C., Scheines, R. (1986). Causal modeling with the TETRAD program. Synthese, 68, 37–64.Google Scholar
  112. Glymour, C., Scheines, R., Spirtes, P., Kelly K. (1987). Discovering causal structure: artificial intelligence, philosophy of science, and statistical modelling. San Diego: Academic.Google Scholar
  113. Goldberger, A. S. (1972). Structural equation methods in the social sciences. Econometrica, 40, 979–1001.Google Scholar
  114. Goldstein, H. (1987). Multilevel models in educational and social research. London: Griffin.Google Scholar
  115. Goldstein, H. (2003). Multilevel statistical models. Kendall's Library of Statistics, 3. London: Arnold.Google Scholar
  116. Good, I. J. (1959). A theory of causality. British Journal for the Philosophy of Science, 9, 307– 310.Google Scholar
  117. Good, I. J. (1961). A causal calculus I. British Journal for the Philosophy of Science, 11, 305– 18. Reprinted in I. J. Good, Good thinking. The foundations of probability and its applications (pp. 197–217). Minneapolis: University of Minnesota Press.Google Scholar
  118. Good, I. J. (1962), “A causal calculus II”, British Journal for the Philosophy of Science, 12, pp. 43– 51. Reprinted in I. J. Good, Good thinking. The foundations of probability and its applications (pp. 197–217). Minneapolis: University of Minnesota Press.Google Scholar
  119. Good, I. J. (1972). Review of Patrick Suppes ‘A Probabilistic Theory of Causality’. Journal of American Statistical Association, 67(337), 245–246.Google Scholar
  120. Good, I. J. (1977). Explicativity: a mathematical theory of explanation with statistical applications. Proceedings of Royal Society, A 354, 303–330. Reprinted in I. J. Good, Good thinking. The foundations of probability and its applications (pp. 219–236). Minneapolis: University of Minnesota Press.Google Scholar
  121. Good, I. J. (1980). Some comments on probabilistic causality. Pacific Philosophical Quarterly, 61, 301–304.Google Scholar
  122. Good, I. J. (1983a). Good thinking. The foundations of probability and its applications. Minneapolis: University of Minnesota Press.Google Scholar
  123. Good, I. J. (1983b). The philosophy of exploratory data analysis. Philosophy of Science, 50, 283– 295.Google Scholar
  124. Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438.Google Scholar
  125. Grawitz, M. (1996). M éthodes des sciences sociales. Paris: Dalloz. Tenth edition.Google Scholar
  126. Greenland, S. (2000). An introduction to instrumental variables for epidemiologists. International Journal of Epidemiology, 29, 722–729.Google Scholar
  127. Grünbaum, A. (1963). Philosophical problems of space and time. New York: Alfred A. Knopf.Google Scholar
  128. Gutiérrez-Fisac, J. L., Regidor, E., Banegas Banegas, J. R., Rodriguez Artalejo, F. (2002). The size of obesity differences associated with educational level in Spain, 1987, and 1995/97. Journal of Epidemiology and Community Health, 56, 457–460.Google Scholar
  129. Haavelmo, T. (1944). The probability approach in econometrics. Econometrica, 12, iii–vi + 1–115.Google Scholar
  130. Hacking, I. (1965). Logic of statistical inference. London: Cambridge University Press.Google Scholar
  131. Hacking, I. (1978). The emergence of probability: a philosophical study of early ideas about probability, induction and statistical inference. Cambridge: Cambridge University Press.Google Scholar
  132. Hage, J., Meeker, B. F. (1988). Social causality. Boston: Unwin Hyman.Google Scholar
  133. Hájek, A. (2003). Interpretations of probability. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Summer 2003 edition). http://plato.stanford.edu/archives/sum2003/entries/probability-interpret/. Accessed 14 March 2008.
  134. Halpern, J., Pearl, J. (2005a). Causes and explanations: a structural-model approach. Part I: Causes. British Journal for the Philosophy of Science, 56, 843–887.Google Scholar
  135. Halpern, J., Pearl, J. (2005b). Causes and explanations: a structural-model approach. Part II: Explanations. British Journal for the Philosophy of Science, 56, 889–911.Google Scholar
  136. Hanson, N. R. (1958). Patterns of discovery: an inquiry into the conceptual foundations of science. Cambridge: Cambridge University Press.Google Scholar
  137. Hausman, D. (1983). Are there causal relations among dependent variables? Philosophy of Science, 50, 58–81.Google Scholar
  138. Hausman, D. (1993). Linking causal and explanatory asymmetry. Philosophy of Science, 60, 435– 451.Google Scholar
  139. Hausman, D. (1998). Causal asymmetries. Cambridge: Cambridge University Press.Google Scholar
  140. Hausman, D., Woodward, J. (1999). Independence, invariance, and the causal Markov condition. British Journal for the Philosophy of Science, 50, 521–583.Google Scholar
  141. Hausman, D., Woodward, J. (2004). Modularity and the causal Markov condition: a restatement. British Journal for the Philosophy of Science, 55, 147–161.Google Scholar
  142. Heckman, J. (2005). The scientific model of causality. Sociological Methodology, 35(1), 1–97.Google Scholar
  143. Hedström, P., Swedberg, R. (1999a) (Ed.). Social mechanisms: an analytical approach to social theory. Cambridge: Cambridge University Press.Google Scholar
  144. Hedström, P., Swedberg, R. (1999b). Social mechanisms: an introductory essay. In P. Hedström, R. Swedberg (Ed.), Social mechanisms: an analytical approach to social theory (pp. 1–31). Cambridge: Cambridge University Press.Google Scholar
  145. Hellevik, O. (1984). Introduction to causal analysis. London: Allen & Unwin.Google Scholar
  146. Hempel, C. G. (1965). Aspects of scientific explanation and other essays. New York: Free Press.Google Scholar
  147. Hempel, C. G., Oppenheim, P. (1948). Studies in the logic of explanation. Philosophy of Science, 15(2), 135–175. Reprinted in C. G. Hempel, Aspects of scientific explanation and other essays (pp. 245–282). New York: Free Press.Google Scholar
  148. Hesslow, G. (1976). Discussion: two notes on the probabilistic approach to causality. Philosophy of Science, 43, 290–292.Google Scholar
  149. Hitchcock, C. (2001). The intransitivity of causation revealed in equations and graphs. Journal of Philosophy, 98(6), 273–299.Google Scholar
  150. Hitchcock, C. (2004) (Ed.). Contemporary debates in philosophy of science. Oxford: Blackwell.Google Scholar
  151. Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945–970.Google Scholar
  152. Holland, P. W. (1988). Comment: causal mechanism or causal effect? Which is best for statistical science? Statistical Science, 3(2), 186–188.Google Scholar
  153. Holland, P. W., Rubin, D. B. (1988). Causal inference in retrospective studies. Evaluation Review, 12(3), 203–231.Google Scholar
  154. Hoover, K. D. (2001). Causality in macroeconomics. Cambridge: Cambridge University Press.Google Scholar
  155. Howson, C. (1983). Statistical explanation and statistical support. Erkenntnis, 20, 61–78.Google Scholar
  156. Howson, C. (1988). On a recent argument for the impossibility of a statistical explanation of single events. Erkenntnis, 29, 113–124.Google Scholar
  157. Howson, C. (2001). The logic of Bayesian probability. In D. Corfield, J. Williamson (Ed.), Foundations of Bayesianism (pp. 137–159). Dordrecht: Kluwer.Google Scholar
  158. Howson, C., Urbach, P. (1993). Scientific reasoning: the Bayesian approach. Chicago: Open Court. Second edition.Google Scholar
  159. Hume, D. (1739–1740). A treatise of human nature. Edited by D. F. Norton, M. J. Norton. Oxford/New York: Oxford University Press, 2000.Google Scholar
  160. Hume, D. (1748). An enquiry concerning human understanding. Edited by T. L. Beauchamp. Oxford/New York: Oxford University Press, 1999.Google Scholar
  161. Humphreys, P. (1984). Aleatory explanation expanded. PSA: Proceedings of the Biannual Meeting of the Philosophy of Science Association, 1982, vol. 2, 208–223.Google Scholar
  162. Humphreys, P. (1986a). Causality in the social sciences: an overview. Synthese, 68, 1–12.Google Scholar
  163. Humphreys, P. (1986b). Quantitative probabilistic causality and structural scientific realism. PSA:Proceedings of the Biannual Meeting of the Philosophy of Science Association, 1984, vol. 2, 329–342.Google Scholar
  164. Humphreys, P. (1989). The chances of explanation. Princeton: Princeton University Press.Google Scholar
  165. Irzik, G. (1986). Causal modelling and the statistical analysis of causation. PSA: Proceedings of the Biannual Meeting of the Philosophy of Science Association, 1984, vol. 1: Contributed Papers, 12–23.Google Scholar
  166. Irzik, G. (1996). Can causes be reduced to correlations? British Journal for the Philosophy of Science, 47, 249–270.Google Scholar
  167. Irzik, G., Meyer, E. (1987). Causal modeling: new directions for statistical explanation. Philosophy of Science, 54, 495–514.Google Scholar
  168. Jaynes, E. T. (1957). Information theory and statistical mechanics. The Physical Review, 106(4), 620–630. Reprinted in E. T. Jaynes, E. T. Jaynes: papers on probability, statistics and statistical physics (pp. 4–16). Edited by R. G. Rosenkrantz. Dordrecht: Kluwer.Google Scholar
  169. Jaynes, E. T. (1989). E. T. Jaynes: papers on probability, statistics and statistical physics. Edited by R. G. Rosenkrantz. Dordrecht: Kluwer.Google Scholar
  170. Jaynes, E. T. (2003). Probability theory: the logic of science. Cambridge: Cambridge University Press.Google Scholar
  171. Jeffrey, R. (1966). The logic of decision. Chicago: University of Chicago Press. Second edition 1983.Google Scholar
  172. Jeffreys, H. (1939). Theory of probability. Reprinted, Oxford: Oxford University Press. Oxford Classics in the Physical Sciences Series. 1998.Google Scholar
  173. Kalleberg, A. L. (1977). Work values and job rewards: a theory of job satisfaction. American Sociological Review, 42, 124–143.Google Scholar
  174. Kant, I. (1781). Critique of pure reason. Second edition 1787. Translated by N. K. Smith. London: McMillan. 1929.Google Scholar
  175. Kant, I. (1783). Prolegomena to any future metaphysics. Translated by P. Gray Lucas. Manchester: Manchester University Press. 1953.Google Scholar
  176. Keynes, J. M. (1921). A treatise on probability. London: Macmillan.Google Scholar
  177. Kincaid, H. (1990). Defending laws in the social sciences. Philosophy of the Social Sciences, 20, 56–83.Google Scholar
  178. Kincaid, H. (2004). There are laws in the social sciences. In C. Hitchcock (Ed.), Contemporary debates in philosophy of science (pp. 168–186). Oxford: Blackwell.Google Scholar
  179. Klein, L. R. (1974). A textbook of econometrics. Englewood Cliffs (N.J): Prentice-Hall. Second edition.Google Scholar
  180. Kolmogorov, A. N. (1933). Foundations of the theory of probability. New York: Chelsea Publishing. 1950.Google Scholar
  181. Koopman, B. O. (1940). The axioms and algebra of intuitive probability. Annals of Mathematics, 41, 269–292.Google Scholar
  182. Korb, K., Wallace, C. (1997). In search of the philosopher's stone: remarks on Humphreys and Freedman's critique of causal discovery. British Journal for the Philosophy of Science, 48(4), 543–553.Google Scholar
  183. Kundi, M. (2006). Causality and the interpretation of epidemiological evidence. Environmental Health Perspectives, 114, 969–974.Google Scholar
  184. Kyburg, H. E., Smokler, H. E., (1964) (Ed.). Studies in subjective probability. New York: Wiley.Google Scholar
  185. Lagiou, P., Adami, H. -O., Trichopoulos, D. (2005). Causality in cancer epidemiology. European Journal of Epidemiology, 20, 565–574.Google Scholar
  186. Land, K. C. (1983). Social indicators. Annual Review of Sociology, 9, 1–26.Google Scholar
  187. Laplace, P. (1814). Essai philosophique sur les probabilité s. Paris: Bourgois. 1986.Google Scholar
  188. Larson, J. (1991). The measurement of health: concepts and indicators. New York: Greenwood Press.Google Scholar
  189. Laudisa, F. (1999). Causalità. Storia di un modello di conoscenza. Roma: Carocci Editore.Google Scholar
  190. Lehnmann, E.L. (1966). Some concepts of dependence. Annals of Mathematical Statistics, 37, 1137–1153.Google Scholar
  191. Lewis, D. (1971). A subjectivist guide to objective chance. Reprinted in D. Lewis, Philosophical papers Vol. II (pp. 159–213). Oxford: Oxford University Press. 1986.Google Scholar
  192. Lewis, D. (1973). Causation. Reprinted with postscripts in D. Lewis, Philosophical Papers Vol. II (pp. 159–213). Oxford: Oxford University Press.Google Scholar
  193. Lilienfeld, D. E., Stolley, P. D. (1994). Foundations of epidemiology. New York: Oxford University Press. Third edition.Google Scholar
  194. Little, D. (1990). Varieties of social explanations: an introduction to the philosophy of social science. Boulder: Westview Press.Google Scholar
  195. Little, D. (1993). On the scope and limits of generalizations in the social sciences. Synthese, 97, 183–208.Google Scholar
  196. Little, D. (1995a). Causal explanation in the social sciences. Southern Journal of Philosophy, 34 (Supplement), 31–56.Google Scholar
  197. Little, D. (1995b) (Ed.). On the reliability of economic models: essays in the philosophy of economics. Boston: Kluwer.Google Scholar
  198. Little, D. (1998). Microfoundations, method and causation. On the philosophy of the social sciences. New Brunswick, NJ: Transaction Publishers.Google Scholar
  199. Little, D. (2004). Causal mechanisms. In M. S. Lewis-Beck, A. Bryman, T. F. Liao (Ed.), The Sage Encyclopedia of social science research methods, Vol. 1. Thousand Oaks, CA: Sage.Google Scholar
  200. Long, J. S. (1983a). Confirmatory factor analysis. Beverly Hills: Sage.Google Scholar
  201. Long, J. S. (1983b). Covariance structure models. Beverly Hills: Sage.Google Scholar
  202. López-Ríos, O., Mompart, A., Wunsch, G. (1992). Système de soins et mortalité régionale: une analyse causale. European Journal of Population, 8(4), 363–379.Google Scholar
  203. Mach, E. (1905). Knowledge and error. Dordrecht: Reidel. 1976.Google Scholar
  204. Mackie, J. L. (1974). The cement of the universe: a study on causation. Oxford: Clarendon.Google Scholar
  205. Masuy-Stroobant, G. (2002). The determinants of infant mortality: how far are conceptual frameworks really modelled? In R. Franck (Ed.), The explanatory power of models (pp. 15–30). Dordrecht: Kluwer.Google Scholar
  206. Masuy-Stroobant, G., Gourbin, C. (1995). Infant health and mortality indicators: their accuracy for monitoring the socio-economic development in the Europe of 1994. European Journal of Population, 11(1), 63–84.Google Scholar
  207. Maudlin, T. (2007). The metaphysics within physics. Oxford: Clarendon.Google Scholar
  208. McCullag, P. (1989) (Ed.). Generalized linear models. London: Chapaman & Hall.Google Scholar
  209. McKim, V., Turner, S. P. (1997) (Ed.). Causality in crisis? Statistical methods and the search for causal knowledge in the social sciences. Indiana: University of Notre Dame Press.Google Scholar
  210. Meek, C. (1995). Causal inference and causal explanation with background knowledge. In P. Besnard, S. Hanks (Ed.), Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (pp. 403–410). San Mateo, CA: Morgan Kaufmann.Google Scholar
  211. Mellor, D. H. (1971). The matter of chance. London: Cambridge University Press.Google Scholar
  212. Menzies, P., Price, H. (1993). Causation as a secondary quality. British Journal for the Philosophy of Science, 44, 187–203.Google Scholar
  213. Mill, J. S. (1843). A system of logic, ratiocinative and inductive: being a connected view of the principles of evidence and the methods of scientific investigation. New York: Harper & Brothers. Eighth edition.Google Scholar
  214. Montuschi, E. (2003). The objects of social science. London: Continuum.Google Scholar
  215. Montuschi, E. (2006). Oggettività e scienze umane. Introduzione alla filosofia della ricerca sociale. Roma: Carocci editore.Google Scholar
  216. Mooney Marini, M., Singer, B. (1988). Causality in the social sciences. Sociological Methodology, 18, 347–409.Google Scholar
  217. Morgan, M. (1997). Searching for causal relations in economic statistics. In V. McKim, S. P. Turner (Ed.), Causality in crisis? Statistical methods and the search for causal knowledge in the social sciences (pp. 47–80). Indiana: University of Notre Dame Press.Google Scholar
  218. Mosley, W. H., Chen, L. C. (1984). An analytical framework for the study of child survival in developing countries. Population and Development Review, 10 (Supplement), 25–45.Google Scholar
  219. Mouchart, M., Russo, F., Wunsch, G. (2008). Structural modelling, exogeneity and causality. In H. Engelhardt, H. -P. Kohler, A. Prskwetz (Ed.), Causal analysis in population studies: concepts, methods, applications (Chapter 4). Dordrecht: Springer.Google Scholar
  220. Mulaik, S. A. (1972). The foundations of factor analysis. New York: McGraw Hill.Google Scholar
  221. Mulaik, S. A. (1985). Exploratory statistics and empiricism. Philosophy of Science, 52, 410–430.Google Scholar
  222. Neter, J., Kutner, M. H., Nachtsheim C. J., Wasserman, W. (1996). Applied linear statistical models. Chicago: Richard D. Irwin. Fourth edition.Google Scholar
  223. Niiniluoto, I. (1981). Statistical explanation reconsidered. Synthese, 48, 437–472.Google Scholar
  224. Norris, P., Inglehart, R. (2003). Islam and the West: testing the ‘clash of civilization‘ thesis. Comparative Sociology, 1(3–4), 235–265. http://ksghome.harvard.edu/˜pnorris/Acrobat/ Clash%20of%20Civilization.pdf. Accessed 14 March 2008.Google Scholar
  225. Otte, R. (1981). A critique of Suppes‘ theory of probabilistic causality. Synthese, 48, 167–189.Google Scholar
  226. Papineau, D. (1985). Causal asymmetry. British Journal for Philosophy of Science, 36(3), 273– 289.Google Scholar
  227. Papineau, D. (1991). Correlation and causes. British Journal for Philosophy of Science, 42(3), 397–412.Google Scholar
  228. Parascandola, M., Weed, D. (2001). Causation in epidemiology. Journal of Epidemiology and Community Health, 55, 905–912.Google Scholar
  229. Pearl, J. (1988a). Probabilistic reasoning in intelligent systems. San Mateo, CA: Morgan Kaufman.Google Scholar
  230. Pearl, J. (1988b). Graphs, causality, and structural equation models. Sociological Methods and Research, 27(2), 226–284.Google Scholar
  231. Pearl, J. (1990). Jeffrey‘s rule, passage of experience, and neo-Bayesianism. In H. E. Kyburg et al. (Ed.), Knowledge representation and defeasible reasoning (pp. 245–265). Amsterdam: Kluwer.Google Scholar
  232. Pearl, J. (1995). Causal diagrams for empirical research. Biometrika, 82(4), 669–688.Google Scholar
  233. Pearl, J. (2000). Causality. Models, reasoning, and inference. Cambridge: Cambridge University Press.Google Scholar
  234. Pearl, J. (2001). Causal inference in statistics: a gentle introduction. Technical Report R-289, Computer Science Department, University of California, Los Angeles.Google Scholar
  235. Pearl, J., Verna, T. S. (1991). A statistical semantics for causation. In Proceeding, 3rd International Workshop on AI & Statistics, Fort Lauderdale, FL, 2–5 January 1991. http://bayes.cs.ucla.edu/cslpapers.html. Accessed 14 March 2008.
  236. Pearson, K. (1911). The grammar of science. London: A. and C. Black.Google Scholar
  237. Peto, R., Darby, S., Deo, H., Silcocks, P., Whitley, E., Doll, R. (2000). Smoking, smoking cessation, and lung cancer in UK since 1950. British Medical Journal, 321, 323–329.Google Scholar
  238. Philips, D., Berman, Y. (2003). Social quality and ethnos communities: concepts and indicators. Community Development Journal, 38, 344–357.Google Scholar
  239. Pickett, K., Pearl, M. (2001). Multilevel analysis of neighbourhood socioeconomic context and health outcomes: a critical review. Journal of Epidemiology and Community Health, 55, 111– 122.Google Scholar
  240. Popper, K. R. (1957). The propensity interpretation of the calculus of probability and the quantum theory. In S. Körner (Ed.), The Colston Papers (pp. 65–70), vol. 9.Google Scholar
  241. Popper, K. R. (1959). The propensity interpretation of probability. British Journal for the Philosophy of Science, 10, 25–42.Google Scholar
  242. Pratt, J. W., Schlaifer, R. (1984). On the nature and discovery of structure. Journal of the American Statistical Association, 79(385), 9–21.Google Scholar
  243. Price, H. (1991). Agency and probabilistic causality. British Journal for the Philosophy of Science, 42, 157–176.Google Scholar
  244. Price, H. (1992). Agency and causal asymmetry. Mind, 101, 501–520.Google Scholar
  245. Price, H. (2004). Models and modals. In D. Gillies (Ed.), Laws and models in science (pp. 49–69). London: King‘s College Publications.Google Scholar
  246. Psillos, S. (2002). Causation and explanation. Chesham: Acumen Publishing.Google Scholar
  247. Psillos, S. (2004). A glimpse of the secret connexion: harmonizing mechanisms with counterfac-tuals. Perspectives on Science, 12(3), 288–319.Google Scholar
  248. Psillos, S. (2005). Undetermination. Encyclopedia of Philosophy. Gale MacMillan Reference. Second edition. http://www.phs.uoa.gr/˜psillos/Publicationsfiles/Underdetermination.doc. Accessed 14 March 2008.
  249. Quetelet, A. (1869). Physique sociale. Ou essai sur le développement des facultés de l‘homme. Bruxelles: Muquardt.Google Scholar
  250. Ramsey, F. P. (1931). Truth and probability. In F. P. Ramsey, The foundations of mathematics and other logical essays (Chapter VII, pp. 156–198). Edited by R. B. Braithwaite. London/London: Kegan Paul/Harcourt, Brace & Co. 1999 electronic edition. http://socserv.mcmaster.ca/econ/ugcm/3ll3/ramseyfp/ramsess.pdf. Accessed 14 March 2008.
  251. Reichenbach, H. (1949). The theory of probability. Berkeley: University of California Press.Google Scholar
  252. Reichenbach, H. (1956). The direction of time. Berkeley: University of California Press.Google Scholar
  253. Reiss, J. (2001). Natural economic quantities and their measurement. Measurement in Physics and Economics — Discussion Papers. Technical Report 14/01, Centre for Philosophy of Natural and Social Science, London School of Economics.Google Scholar
  254. Reiss, J. (2007). Do we need mechanisms in the social sciences? Philosophy of the Social Sciences, 37(2), 163–184.Google Scholar
  255. Reiss, J. (2008). Error in economics: towards a more evidence-based methodology. London: Rout-ledge.Google Scholar
  256. Roberts, J. T. (2004). There are no laws in the social sciences. In C. Hitchcock (Ed.), Contemporary debates in the social sciences (pp. 151–167). Oxford: Balckwell.Google Scholar
  257. Robinson, W. (1950). Ecological correlations and the behaviour of individuals. American Sociological Review, 15, 351–357.Google Scholar
  258. Rosen, D. (1978). In defence of a probabilistic theory of causality. Philosophy of Science, 45, 604–613.Google Scholar
  259. Rosenbaum, P. R. (1984). From association to causation in observational studies. The role of tests of strongly ignorable treatment assignment. Journal of the American Statistical Association, 385, 41–48.Google Scholar
  260. Rosenbaum, P. R., Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.Google Scholar
  261. Rubin, D. (1974). Estimating causal effects of treatments in randomized and non randomized studies. Journal of Educational Psychology, 66(5), 688–701.Google Scholar
  262. Rubin, D. (1978). Bayesian inference for causal effects: the role of randomization. The Annals of Statistics, 6, 34–58.Google Scholar
  263. Russell, B. (1912–1913). On the notion of cause. Proceedings of the Aristotelian Society, 13, 1–26.Google Scholar
  264. Russo, F. (2006a). Salmon and van Fraassen on the existence of unobservable entities: a matter of interpretation of probability. Foundations of Science, 11(3), 221–247.Google Scholar
  265. Russo, F. (2006b). The rationale of variation in methodological and evidential pluralism. Philo-sophica, 77, 97–124.Google Scholar
  266. Russo, F. (2007). Frequency-driven probabilities in quantitative causal analysis. Philosophical Writings, 32, 32–56.Google Scholar
  267. Russo, F., Mouchart, M., Ghins, M., Wunsch, G. (2006). Causality, structural modelling and exogeneity. Discussion Paper 0601, Institut de Statistique, Université catholique de Louvain, Belgium.Google Scholar
  268. Russo, F., Williamson J. (2007a) (Ed.). Causality and probability in the sciences. Texts in Philosophy Series. London: College Publications.Google Scholar
  269. Russo, F., Williamson, J. (2007b). Interpreting probability in causal models for cancer. In F. Russo, J. Williamson (Ed.), Causality and probability in the sciences (pp. 217–242). Texts in Philosophy Series. London: College Publications.Google Scholar
  270. Russo, F., Williamson, J. (2007c). Interpreting causality in the health sciences. International Studies in Philosophy of Science, 21(2), 157–170.Google Scholar
  271. Salmon, W. C. (1967). Foundations of scientific inference. Pittsburgh: University of Pittsburgh Press.Google Scholar
  272. Salmon, W. C. (1971). Statistical explanation. In W. C. Salmon et al. (Ed.), Statistical explanation and statistical relevance (pp. 29–87). Pittsburgh: University of Pittsburgh Press.Google Scholar
  273. Salmon, W. C. (1977). Objectively homogeneous references classes. Synthese, 36, 399–414.Google Scholar
  274. Salmon, W. C. (1980). Probabilistic causality. Pacific Philosophical Quarterly, 61, 50–74.Google Scholar
  275. Salmon, W. C. (1984). Scientific explanation and the causal structure of the world. Princeton, NJ: Princeton University Press.Google Scholar
  276. Salmon, W. C. (1988). Dynamic rationality. In J. Fetzer J. (Ed.), Probability and causality (pp. 3–42). Dordrecht: Reidel.Google Scholar
  277. Salmon, W. C. (1990). Causal propensities: statistical causality vs. aleatory causality. Topoi, 9, 95–100.Google Scholar
  278. Salmon, W. C. (1998). Causality and explanation. New York: Oxford University Press.Google Scholar
  279. Salmon, W. C. et al. (1971). Statistical explanation and statistical relevance. Pittsburgh: University of Pittsburgh Press.Google Scholar
  280. Savage, L. J. (1954). The foundations of statistics. New York: Wiley.Google Scholar
  281. Simon, H. (1952). On the definition of the causal relation. Journal of Philosophy, 49(16), 517– 528.Google Scholar
  282. Simon, H. (1953). Causal ordering and identifiability. In W. C. Hood, T. C. Koopmans (Ed.), Studies in econometric method (pp. 49–74). New York: Wiley.Google Scholar
  283. Simon, H. (1954). Spurious correlation: a causal interpretation. Journal of the American Statistical Association, 49(267), 467–479.Google Scholar
  284. Simon, H. (1979). The meaning of causal ordering. In K. R. Merto, J. J. Coleman, P. H. Rossi (Ed.), Qualitative and quantitative social research (pp. 65–81). New York: Free Press.Google Scholar
  285. Skyrms, B. (1988). Probability and causation. Journal of Econometrics, 39, 53–68.Google Scholar
  286. Snijders, T. A. B., Bosker, R. J. (2004). Multilevel analysis. An introduction to basic and advanced multilevel modeling. London: Sage. Fourth edition.Google Scholar
  287. Sober, E. (1984). Two concepts of cause. PSA: Proceedings of the Biannual Meeting of the Philosophy of Science Association, 1982, vol. 2, 405–424.Google Scholar
  288. Sober, E. (1986). Causal factors, causal influence, causal explanation. Proceedings of Aristotelian Society, 60, 97–136.Google Scholar
  289. Spirtes, P., Glymour, C., Scheines, R. (1991). From probability to causality. Philosophical Studies, 91, 1–36.Google Scholar
  290. Spirtes, P., Glymour, C., Scheines, R. (1993). Causation, prediction, and search. New York: Springer.Google Scholar
  291. Spirtes, P., Richardson, T., Meek, C., Scheines, R., Glymour, C. (1998). Using path diagrams as a structural equation modeling tool. Sociological Method and Research, 27(2), 182–225.Google Scholar
  292. Steuer, M. (2003). The scientific study of society. Boston: Kluwer.Google Scholar
  293. Stone, R. (1993). The assumptions on which causal inferences rest. Journal of the American Statistical Association, 55(2), 455–466.Google Scholar
  294. Suppes, P. (1970). A probabilistic theory of causality. Amsterdam: North Holland.Google Scholar
  295. Suppes, P. (1982). Problems of causal analysis in the social sciences. Epistemologia, Special Issue 1982, 239–250.Google Scholar
  296. Suppes, P. (2002). Representation and invariance of scientific structures. Stanford: CSLI Publications.Google Scholar
  297. Susser, M. (2001). Glossary: causality in public health sciences. Journal of Epidemiology and Community Health, 55, 376–378.Google Scholar
  298. Susser, M. W. (1973). Causal thinking in the health sciences. New York: Oxford University Press.Google Scholar
  299. Susser, M., Susser, E. (1996). Choosing a future for epidemiology ii: from black box to Chinese box and ecoepidemiology. American Journal of Public Health, 86, 674–677.Google Scholar
  300. Thompson, P. (2006). Bayes p-values. In S. Kotz, C. B. Read, N. Balakrishnan, B. Vidakovic (Ed.), Encyclopedia of Statistical Sciences. Hoboken, NJ: Wiley-Interscience. Second edition.Google Scholar
  301. Toulemon, L. (2006). Les modèles de régression. In G. Caselli, J. Vallin, G. Wunsch (Ed.), D émographie: analyse et synthèse (pp. 359–374). Volume VIII Observation, méthodes auxiliaires, enseignement et recherche. Paris: Editions de l‘Institut National d‘Etudes Démographiques.Google Scholar
  302. Urbach, P. (1989). Random sampling and the principles of estimation. Proceedings of the Aristotelian Society, 89, 143–164.Google Scholar
  303. Vandresse, M. (2005). Characteristics of the newborn at birth and health status: a congested two-frontiers approach. Unpublished manuscript.Google Scholar
  304. Vandresse, M. (2008). Late fertility: its causal effects on health of the newborn and its implications in fertility decision process. PhD Thesis. Université catholique de Louvain.Google Scholar
  305. van Bouwel, J. (2004). Individualism and holism, reduction and pluralism: a comment on Keith Sawyer and Julie Zahle. Philosophy of the Social Sciences, 34(4), 527–535.Google Scholar
  306. van Fraassen, B. C. (1980). The scientific image. Oxford: Clarendon Press.Google Scholar
  307. van Fraassen, B. C. (1983). Calibration: a frequency justification for personal probabilities. In R. S. Cohen, L. Laudan (Ed.), Physics, philosophy and psychoanalysis: essays in honour of Adolph Grümbaum (pp. 295–319). Dordrecht: Reidel.Google Scholar
  308. van Fraassen, B. C. (1989). Laws and symmetry. Oxford: Clarendon.Google Scholar
  309. Venn, J. (1876). The logic of chance: an essay on the foundations and province of the theory of probability. London: Macmillan. Second edition.Google Scholar
  310. Vineis, P. (2003). Causality in epidemiology. Sozial und Praventiv Medizin, 48, 80–87.Google Scholar
  311. von Bertalanffy, L. (1969). General system theory: foundations, development, applications. New York: Braziller.Google Scholar
  312. von Mises, R. (1957). Probability, statistics and truth. New York: Macmillan. Second edition.Google Scholar
  313. von Wright, G. (1971). Explanation and understanding. Ithaca: Cornell University Press.Google Scholar
  314. Wackerly, D. D., Mendenhall W., Scheaffer R. L. (2002). Mathematical statistics with applications. Pacific Grove: Duxbury. Sixth edition.Google Scholar
  315. Williams, M., Williamson, J. (2006). Combining argumentation and Bayesian nets for breast cancer prognosis. Journal of Logic, Language and Information, 15, 155–178.Google Scholar
  316. Williamson, J. (2005a). Bayesian nets and causality. Philosophical and computational foundations. Oxford: Oxford University Press.Google Scholar
  317. Williamson, J. (2005b). Causality. In D. Gabbay, F. Guenthner (Ed.), Handbook of philosophical logic (pp. 131–162). volume 13. Springer.Google Scholar
  318. Williamson, J. (2005c). Philosophies of probability: objective Bayesianism and its challenges. In A. Irvine (Ed.), Handbook of the philosophy of mathematics. Volume four of Handbook of Philosophy of Science. Elsevier. http://www.kent.ac.uk/secl/philosophy/jw/2004/philprob.pdf. Accessed 17 March 2008.
  319. Williamson, J. (2007). Motivating objective Bayesianism: from empirical constraints to objective probabilities. In W. L. Harper, G. R. Wheeler (Ed.), Probability and inference: essays in honour of Henry E. Kyburg Jr (pp. 155–183). London: College Publications.Google Scholar
  320. Woodward, J. (1993). Book review: Humphreys, P., ‘The chances of explanation: causal explanation in social, medical and physical sciences‘. Philosophy of Science, 60(4), 671–673.Google Scholar
  321. Woodward, J. (1999). Causal interpretation in systems of equations. Synthese, 121, 199–247.Google Scholar
  322. Woodward, J. (2003). Making things happen: a theory of causal explanation. Oxford: Oxford University Press.Google Scholar
  323. Wonnacott, T. H., Wonnacott, R. J. (1990). Introductory Statistics. New York: Wiley. Fifth edition.Google Scholar
  324. Worrall, J. (2002). What evidence in evidence-based medicine? Philosophy of Science, 69, 316– 330.Google Scholar
  325. Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20, 557–585.Google Scholar
  326. Wright, S. (1934). The method of path coefficient. Annals of Mathematical Statistics, 5(3), 161– 215.Google Scholar
  327. Wunsch, G. (1984). Theories, models and knowledge: the logic of demographic discovery. Genus, 40(1–2), 1–18.Google Scholar
  328. Wunsch, G. (1986). Causal connections in causal modelling. Genus, 42(3–4), 1–12.Google Scholar
  329. Wunsch, G. (1988). Causal theory and causal modelling. Leuven: Leuven University Press.Google Scholar
  330. Wunsch, G. (1995). God has chosen to give the easy case to the physicists. In Evolution or revolution in European population. European Population Conference. 1 Plenary Session (pp. 201– 224). Milano: Franco Angeli.Google Scholar
  331. Wunsch, G. (2007). Confounding and control. Demographic research, 6(4), 95–120.Google Scholar
  332. Yule, G. U. (1897). On the theory of correlation. Journal of Royal Statistical Society, 60, 812–854.Google Scholar
  333. Zellner, A. (1988). Causality and causal laws in economics. Journal of Econometrics, 39, 7–21.Google Scholar
  334. Zeller, R. A., Carmines, E. G. (1980). Measurement in the social science. The link between theory and data. Cambridge: Cambridge University Press.Google Scholar

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