Abstract
In the first part of this chapter I finish the axiomatization of the causal nets framework started in Chap. 3 I also argue that the causal Markov axiom provides the best explanation for two statistical phenomena. In the second part I present several results about the empirical content of different versions (i.e., combination of axioms) of the theory of causal nets. Both parts together show that causation satisfies the same modern standards as theoretical concepts of good empirical theories do. This can be seen as new empirical support for the theory of causal nets, but also as an answer to Hume’s skeptical challenge: Actually, it seems that we have good reasons to believe in causation as something ontologically real out there in the world.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
This constraint is required to distinguish synthetic causal dependencies from analytic dependencies such as dependencies due to meaning, definition, conceptualization, etc.
- 3.
For a more detailed explanation why we would find exactly the mentioned probabilistic dependence and independence relations, see Gebharter (2013, p. 66).
- 4.
I call causal systems satisfying the three mentioned conditions “robustly” producing (or underlying) the corresponding empirical systems since these causal systems presuppose robust (or faithful) probability distributions. In the next subsection, I will also introduce underlying systems for explaining (in)dependencies in empirical systems without robust probability distributions.
References
Balzer, W., Moulines, C. U., & Sneed, J. D. (1987). An architectonic for science. Dordrecht: Springer.
Baumgartner, M. (2009). Interventionist causal exclusion and non-reductive physicalism. International Studies in the Philosophy of Science, 23(2), 161–178.
Baumgartner, M. (2010). Interventionism and epiphenomenalism. Canadian Journal of Philosophy, 40(3), 359–383.
Baumgartner, M., & Casini, L. (2016). An abductive theory of constitution. Philosophy of Science.
Baumgartner, M., & Gebharter, A. (2016). Constitutive relevance, mutual manipulability and fat-handedness. British Journal for the Philosophy of Science, 67(3), 731–756.
Bechtel, W. (2007). Reducing psychology while maintaining its autonomy via mechanistic explanation. In M. Schouten & H. L. de Jong (Eds.), The matter of the mind: Philosophical essays on psychology, neuroscience, and reduction (pp. 172–198). Oxford: Blackwell.
Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36, 421–441.
Bechtel, W., & Richardson, R. C. (2000). Discovering complexity: Decomposition and localization as scientific research strategies. Princeton: Princeton University Press.
Beebee, H., Hitchcock, C., & Menzies, P (Eds.). (2009). The Oxford handbook of causation. Oxford: Oxford University Press.
Blalock, H. M. (1961). Correlation and causality: The multivariate case. Social Forces, 39(3), 246–251.
Campbell, J. (2007). An interventionist approach to causation in psychology. In A. Gopnik & L. E. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 58–66). Oxford: Oxford University Press.
Carnap, R. (1928/2003). The logical structure of the world and pseudoproblems in philosophy. Chicago: Open Court.
Carnap, R. (1956). The methodological character of theoretical concepts. In H. Feigl & M. Scriven (Eds.), The foundations of science and the concepts of psychology and psychoanalysis (pp. 38–76). Minneapolis: University of Minnesota Press.
Cartwright, N. (1979). Causal laws and effective strategies. Noûs, 13(4), 419–437.
Cartwright, N. (1989). Nature’s capacities and their measurement. Oxford: Oxford University Press.
Cartwright, N. (1999a). Causal diversity and the Markov condition. Synthese, 121(1/2), 3–27.
Cartwright, N. (1999b). The dappled world. Cambridge: Cambridge University Press.
Cartwright, N. (2001). What is wrong with Bayes nets? The Monist, 84(2), 242–264.
Cartwright, N. (2007). Hunting causes and using them. Cambridge: Cambridge University Press.
Casini, L. (2016). How to model mechanistic hierarchies. Philosophy of Science, 83(5), 946–958.
Casini, L., lllari, P. M., Russo, F., & Williamson, J. (2011). Models for prediction, explanation and control: Recursive Bayesian networks. Theoria – An International Journal for Theory, History and Foundations of Science, 26(70), 5–33.
Clarke, B., Leuridan, B., & Williamson, J. (2014). Modelling mechanisms with causal cycles. Synthese, 191(8), 1651–1681.
Collingwood, R. G. (2002). In R. Martin (Ed.), An essay on metaphysics. Oxford: Clarendon Press.
Craver, C. (2007a). Constitutive explanatory relevance. Journal of Philosophical Research, 32, 3–20.
Craver, C. (2007b). Explaining the brain. Oxford: Clarendon Press.
Craver, C., & Bechtel, W. (2007). Top-down causation without top-down causes. Biology and Philosophy, 22(4), 547–563.
Danks, D., & Plis, S. (2015). Learning causal structure from undersampled time series. In JMLR: Workshop and Conference Proceedings, Hong Kong.
Dawid, A. P. (1979). Conditional independence in statistical theory. Journal of the Royal Statistical Society. Series B (Methodological), 41(1), 1–31.
Dowe, P. (2007). Physical causation. Cambridge: Cambridge University Press.
Eberhardt, F., & Scheines, R. (2007). Interventions and causal inference. Philosophy of Science, 74(5), 981–995.
Eells, E. (1987). Probabilistic causality: Reply to John Dupré. Philosophy of Science, 54(1), 105–114.
Eells, E., & Sober, E. (1983). Probabilistic causality and the question of transitivity. Philosophy of Science, 50(1), 35–57.
Eronen, M. I. (2011). Reduction in philosophy of mind. Heusenstamm: De Gruyter.
Eronen, M. I. (2012). Pluralistic physicalism and the causal exclusion argument. European Journal for Philosophy of Science, 2(2), 219–232.
Fagan, M. (2013). Philosophy of stem cell biology. Basingstoke: Palgrave Macmillan.
Fazekas, P., & Kertesz, G. (2011). Causation at different levels: Tracking the commitments of mechanistic explanations. Biology and Philosophy, 26(3), 365–383.
French, S. (2008). The structure of theories. In S. Psillos & M. Curd (Eds.), The Routledge companion to philosophy of science (pp. 269–280). London: Routledge.
Friedman, M. (1974). Explanation and scientific understanding. Journal of Philosophy, 71(1), 5–19.
Gasking, D. (1955). Causation and recipes. Mind, 64(256), 479–487.
Gebharter, A. (2013). Solving the flagpole problem. Journal for General Philosophy of Science, 44(1), 63–67.
Gebharter, A. (2014). A formal framework for representing mechanisms? Philosophy of Science, 81(1), 138–153.
Gebharter, A. (2015). Causal exclusion and causal Bayes nets. Philosophy and Phenomenological Research. doi: 10.1111/phpr.12247.
Gebharter, A. (2016). Another problem with RBN models of mechanisms. Theoria – An International Journal for Theory, History and Foundations of Science, 31(2), 177–188.
Gebharter, A., & Kaiser, M. I. (2014). Causal graphs and biological mechanisms. In M. I. Kaiser, O. R. Scholz, D. Plenge, & A. Hüttemann (Eds.), Explanation in the special sciences (pp. 55–85). Dordrecht: Springer.
Gebharter, A., & Schurz, G. (2014). How Occam’s razor provides a neat definition of direct causation. In J. M. Mooij, D. Janzing, J. Peters, T. Claassen, & A. Hyttinen (Eds.), Proceedings of the UAI workshop Causal Inference: Learning and Prediction, Aachen.
Gebharter, A., & Schurz, G. (2016). A modeling approach for mechanisms featuring causal cycles. Philosophy of Science, 83(5), 934–945.
Glauer, R. D. (2012). Emergent mechanisms. Münster: Mentis.
Glennan, S. (1996). Mechanisms and the nature of causation. Erkenntnis, 44(1), 49–71.
Glennan, S. (2002). Rethinking mechanistic explanation. Philosophy of Science, 69(3), S342–S353.
Glennan, S. (2009). Mechanisms. In H. Beebee, C. Hitchcock, & P. Menzies (Eds.), The Oxford handbook of causation (pp. 315–325). Oxford: Oxford University Press.
Glymour, C. (2004). Critical notice. British Journal for the Philosophy of Science, 55(4), 779–790.
Glymour, C., Spirtes, P., & Scheines, R. (1991). Causal inference. Erkenntnis, 35(1/3), 151–189.
Good, I. J. (1959). A theory of causality. British Journal for the Philosophy of Science, 9(36), 307–310.
Graßhoff, G., & May, M. (2001). Causal regularities. In W. Spohn, M. Ledwig, & M. Esfeld (Eds.), Current issues in causation (pp. 85–114). Paderborn: Mentis.
Grünbaum, A. (1962). Temporally-asymmetric principles, parity between explanation and prediction, and mechanism versus teleology. Philosophy of Science, 29(2), 146–170.
Harbecke, J. (2015). The regularity theory of mechanistic constitution and a methodology for constitutive inference. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 54, 10–19.
Hausman, D. (1998). Causal asymmetries. Cambridge: Cambridge University Press.
Healey, R. (2009). Causation in quantum mechanics. In H. Beebee, C. Hitchcock, & P. Menzies (Eds.), The Oxford handbook of causation. Oxford: Oxford University Press.
Hempel, C. G. (1958). The theoretician’s dilemma. In C. G. Hempel (Ed.), Aspects of scientific explanation and other essays in the philosophy of science (pp. 173–228). New York: Free Press.
Hitchcock, C. (2010). Probabilistic causation. In E. N. Zalta (Ed.), Stanford encyclopedia of philosophy. Retrieved from https://plato.stanford.edu/archives/win2010/entries/causation-probabilistic/
Hitchcock, C., & Woodward, J. (2003). Explanatory generalizations, part II: Plumbing explanatory depth. Noûs, 37(2), 181–199.
Hoover, K. D. (2001). Causality in macroeconomics. Cambridge: Cambridge University Press.
Hume, D. (1738/1975). A treatise of human nature. Oxford: Clarendon Press.
Hume, D. (1748/1999). An enquiry concerning human understanding. Oxford: Oxford University Press.
Illari, P. M., & Williamson, J. (2012). What is a mechanism? Thinking about mechanisms across the sciences. European Journal for the Philosophy of Science, 2(1), 119–135.
Kaplan, D. M. (2012). How to demarcate the boundaries of cognition. Biology and Philosophy, 27(4), 545–570.
Kistler, M. (2009). Mechanisms and downward causation. Philosophical Psychology, 22(5), 595–609.
Kitcher, P. (1989). Explanatory unification and the causal structure of the world. In P. Kitcher & W. Salmon (Eds.), Scientific explanation (pp. 410–505). Minneapolis: University of Minnesota Press.
Korb, K., Hope, L. R., Nicholson, A. E., & Axnick, K. (2004). Varieties of causal intervention. In Pricai 2004: Trends in Artificial Intelligence, Proceedings (Vol. 3157, pp. 322–331). Berlin: Springer
Lauritzen, S. L., Dawid, A. P., Larsen, B. N., & Leimer, H. G. (1990). Independence properties of directed Markov fields. Networks, 20(5), 491–505.
Leuridan, B. (2012). Three problems for the mutual manipulability account of constitutive relevance in mechanisms. British Journal for the Philosophy of Science, 63(2), 399–427.
Lewis, D. (1970). How to define theoretical terms. Journal of Philosophy, 67(13), 427–446.
Lewis, D. (1973). Causation. Journal of Philosophy, 70(17), 556–567.
Machamer, P., Darden, L., & Craver, C. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25.
Mackie, J. L. (1965). Causes and conditions. American Philosophical Quarterly, 2(4), 245–264.
Mackie, J. L. (1974). The cement of the universe. Oxford: Clarendon Press.
McLaughlin, B., & Bennett, K. (2011). Supervenience. In E. N. Zalta (Ed.), Stanford encyclopedia of philosophy. Retrieved from https://plato.stanford.edu/archives/win2011/entries/supervenience/
Menzies, P., & Price, H. (1993). Causation as a secondary quality. British Journal for the Philosophy of Science, 44(2), 187–203.
Murphy K. P. (2002). Dynamic Bayesian networks. UC Berkeley, Computer Science Division.
Murray-Watters, A., & Glymour, C. (2015). What is going on inside the arrows? Discovering the hidden springs in causal models. Philosophy of Science, 82(4), 556–586.
Näger, P. M. (2016). The causal problem of entanglement. Synthese, 193(4), 1127–1155.
Neapolitan, R. E. (1990). Probabilistic reasoning in expert systems. New York: Wiley.
Neapolitan, R. E. (2003). Learning Bayesian networks. Upper Saddle River: Prentice-Hall.
Norton, J. D. (2009). Is there an independent principle of causality in physics? British Journal for the Philosophy of Science, 60(3), 475–486.
Nyberg, E., & Korb, K. (2006). Informative interventions. Technical Report 2006/204, School of Computer Science, Monash University.
Papineau, D. (1996). Theory-dependent terms. Philosophy of Science, 63(1), 1–20.
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo: Morgan Kaufmann.
Pearl, J. (1995). Causal diagrams for empirical research. Biometrika, 82(4), 669–688.
Pearl, J. (2000). Causality (1st ed.). Cambridge: Cambridge University Press.
Pearl, J., & Dechter, R. (1996). Identifying independencies in causal graphs with feedback. In UAI’96: Proceedings of the Twelfth International Conference on Uncertainty in Artificial Intelligence (pp. 420–426). San Francisco: Morgan Kaufmann.
Pearl, J., & Paz, A. (1985). Graphoids: A graph-based logic for reasoning about relevance relations. UCLA Computer Science Department Technical Report 850038. Advances in Artificial Intelligence-II.
Pearl, J., Verma, T., & Geiger, D. (1990). Identifying independence in Bayesian networks. Networks, 20(5), 507–534.
Price, H. (1991). Agency and probabilistic causality. British Journal for the Philosophy of Science 42(2), 157–176.
Psillos, S. (2009). Regularity theories. In H. Beebee, C. Hitchcock, & P. Menzies (Eds.), The Oxford handbook of causation (pp. 131–157). Oxford: Oxford University Press.
Raatikainen, P. (2010). Causation, exclusion, and the special sciences. Erkenntnis, 73(3), 349–363.
Ramsey J., Gazis, P., Roush, T., Spirtes, P., & Glymour, C. (2002). Automated remote sensing with near infrared reflectance spectra: Carbonate recognition. Data Mining and Knowledge Discovery, 6(3), 277–293.
Reichenbach, H. (1935/1971). The theory of probability. Berkeley: University of California Press.
Reichenbach, H. (1956/1991). The direction of time. Berkeley: University of California Press.
Reutlinger, A. (2012). Getting rid of interventions. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 43(4), 787–795.
Richardson, T. (2009). A factorization criterion for acyclic directed mixed graphs. In J. Bilmes & A. Ng (Eds.), Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, Montreal (pp. 462–470). AUAI Press.
Richardson, T., & Spirtes, P. (2002). Ancestral graph Markov models. Annals of Statistics, 30(4), 962–1030.
Russell, B. (1912). On the notion of cause. Proceedings of the Aristotelian Society, 13, 1–26.
Salmon, W. (1984). Scientific explanation and the causal structure of the world. Princeton: Princeton University Press.
Salmon, W. (1997). Causality and explanation. New York: Oxford University Press.
Schurz, G. (2001). Causal asymmetry independent versus dependent variables, and the direction of time. In W. Spohn, M. Ledwig, & M. Esfeld (Eds.), Current issues in causation (pp. 47–67). Paderborn: Mentis.
Schurz, G. (2008). Patterns of abduction. Synthese, 164(2), 201–234.
Schurz, G. (2013). Philosophy of science: A unified approach. New York: Routledge.
Schurz, G. (2015). Causality and unification: How causality unifies statistical regularities. Theoria – An International Journal for Theory, History and Foundations of Science, 30(1), 73–95.
Schurz, G. (in press). Interactive causes: Revising the Markov condition. Philosophy of Science.
Schurz, G., & Gebharter, A. (2016). Causality as a theoretical concept: Explanatory warrant and empirical content of the theory of causal nets. Synthese, 193(4), 1073–1103.
Shapiro, L. A. (2010). Lessons from causal exclusion. Philosophy and Phenomenological Research, 81(3), 594–604.
Shapiro, L. A., & Sober, E. (2007). Epiphenomenalism – The Do’s and the Don’ts. In G. Wolters & P. Machamer (Eds.), Studies in causality: Historical and contemporary (pp. 235–264). Pittsburgh: University of Pittsburgh Press.
Skyrms, B. (1980). Causal necessity: A pragmatic investigation of the necessity of laws. New Haven: Yale University Press.
Sneed, J. D. (1979). The logical structure of mathematical physics. Dordrecht: Reidel.
Soom, P. (2011). From psychology to neuroscience. Frankfurt: Ontos.
Soom, P. (2012). Mechanisms, determination and the metaphysics of neuroscience. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 43(3), 655–664.
Spirtes, P. (1995). Directed cyclic graphical representations of feedback models. In P. Besnard & S. Hanks (Eds.), Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence (pp. 491–498). San Francisco: Morgan Kaufman.
Spirtes, P., Glymour, C., & Scheines, R. (1993). Causation, prediction, and search (1st ed.). Dordrecht: Springer.
Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search (2nd ed.). Cambridge: MIT Press.
Spirtes, P., Meek, C., & Richardson, T. (1999). An algorithm for causal inference in the presence of latent variables and selection bias. In Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence (pp. 499–506). San Francisco: Morgan Kaufman.
Spohn, W. (2001). Bayesian nets are all there is to causal dependence. In M. C. Galavotti, D. Costantini, & P. Suppes (Eds.), Stochastic dependence and causality (pp. 157–172). Stanford: CSLI Publications.
Spohn, W. (2006). Causation: An alternative. British Journal for the Philosophy of Science, 57(1), 93–119.
Steel, D. (2005). Indeterminism and the causal Markov condition. British Journal for the Philosophy of Science, 56(1), 3–26.
Steel, D. (2006). Homogeneity, selection, and the faithfulness condition. Minds and Machines, 16(3), 303–317.
Strevens, M. (2007). Review of Woodward making things happen. Philosophy and Phenomenological Research, 74(1), 233–249.
Suppes, P. (1970). A probabilistic theory of causality. Amsterdam: North-Holland.
Tian, J., & Pearl, J. (2002). A general identification condition for causal effects. In AAAI-Proceedings, Edmonton (pp. 567–573). AAAI/IAAI.
Tomasello, M. (2009). The cultural origins of human cognition. Cambridge: Harvard University Press.
Verma, T. (1987). Causal networks: Semantics and expressiveness. Technical Report, Cognitive Systems Laboratory, University of California.
von Wright, G. (1971). Explanation and understanding. Ithaca: Cornell University Press.
Williamson, J. (2005). Bayesian nets and causality. Oxford: Oxford University Press.
Williamson, J. (2009). Probabilistic theories of causality. In H. Beebee, C. Hitchcock, & P. Menzies (Eds.), The Oxford handbook of causation (pp. 185–212). Oxford: Oxford University Press.
Williamson, J., & Gabbay D. (2005). Recursive causality in Bayesian networks and self-fibring networks. In D. Gillies (Ed.), Laws and models in the sciences (pp. 173–221). London: Oxford University Press.
Woodward, J. (2002). What is a mechanism? A counterfactual account. Philosophy of Science, 69(3), S366–S377.
Woodward, J. (2003). Making things happen. Oxford: Oxford University Press.
Woodward, J. (2008a). Mental causation and neural mechanisms. In J. Hohwy & J. Kallestrup (Eds.), Being reduced (pp. 218–262). Oxford: Oxford University Press.
Woodward, J. (2008b). Response to Strevens. Philosophy and Phenomenological Research, 77(1), 193–212.
Woodward, J. (2009). Agency and interventionist theories. In H. Beebee, C. Hitchcock, & P. Menzies (Eds.), The Oxford handbook of causation (pp. 234–262). Oxford: Oxford University Press.
Woodward, J. (2011a). Causation and manipulability. In E. N. Zalta (Ed.), Stanford encyclopedia of philosophy. Retrieved from https://plato.stanford.edu/archives/win2011/entries/causation-mani/
Woodward, J. (2011b). Scientific explanation. In E. N. Zalta (Ed.), Stanford encyclopedia of philosophy. Retrieved from https://plato.stanford.edu/archives/win2011/entries/scientific-explanation/
Woodward, J. (2013). Mechanistic explanation: Its scope and limits. Aristotelian Society Supplementary, 87(1), 39–65.
Woodward, J. (2015). Interventionism and causal exclusion. Philosophy and Phenomenological Research, 91(2), 303–347.
Woodward, J., & Hitchcock, C. (2003). Explanatory generalizations, part I: A counterfactual account. Noûs, 37(1), 1–24.
Wright, S. (1921). Correlation and causation. Journal for Agricultural Research, 20(7), 557–585.
Zhang, J. (2008). Causal reasoning with ancestral graphs. Journal of Machine Learning Research, 9, 1437–1474.
Zhang, J., & Spirtes, P. (2008). Detection of unfaithfulness and robust causal inference. Minds and Machines 18(2), 239–271.
Zhang, J., & Spirtes, P. (2011). Intervention, determinism, and the causal minimality condition. Synthese, 182(3), 335–347.
Zhang, J., & Spirtes, P. (2016). The three faces of faithfulness. Synthese, 193(4), 1011–1027.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Gebharter, A. (2017). Causality as a Theoretical Concept. In: Causal Nets, Interventionism, and Mechanisms. Synthese Library, vol 381. Springer, Cham. https://doi.org/10.1007/978-3-319-49908-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-49908-6_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49907-9
Online ISBN: 978-3-319-49908-6
eBook Packages: Religion and PhilosophyPhilosophy and Religion (R0)