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Notes
Inverse covariance or partial correlation methods produce an undirected graph that “marries” the direct causes of a common effect. Such edges have no causal interpretation. By contrast, the undirected skeleton of the partially directed graphs produced by correct causal search algorithms such as GES or PC does not include such edges, and does have a causal interpretation.
A similar argument can be given without assuming overdetermination: if {c1,..., cn} is the minimal sufficient set for E, then if E had not occurred, it does not follow that a particular ci would not have occurred.
Alas, much of philosophy of science in the last century debated unprofitably what whether such sentences have truth values and what they could mean.
References
Danks, D., & Plis, S. (2013). Learning causal structure from undersampled time series. In JMLR: Workshop and conference proceedings (Vol. 1, pp. 1–10).
Danks, D. (2014). Unifying the mind. Cambridge: MIT Press.
Davidson, D. (1970). How is weakness of the will possible? In Joel Feinberg (Ed.), Moral concepts. Oxford: Oxford University Press.
Dennett, D. C. (2005). Cognitive wheels: The frame problem of AI. Language and Thought, 3, 217.
Eberhardt, F. (2015). Green and grue causal variables. Synthese. doi:10.1007/s11229-015-0832-z.
Eells, E. (1982). Rational decision and causality. Cambridge: Cambridge University Press.
Field, H. (2003). Causation in a physical world. In M. J. Loux & D. W. Zimmerman (Eds.), Oxford handbook of metaphysics (pp. 435–460). Oxford: Oxford University Press.
Gaifman, H., Osherson, D. N., & Weinstein, S. (1990). A reason for theoretical terms. Erkenntnis, 32(2), 149–159.
Gates, K. M., & Molenaar, P. C. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. Neuroimage, 63(1), 310–319.
Gigerenzer, G. (2010). Moral satisficing: Rethinking moral behavior as bounded rationality. Topics in Cognitive Science, 2(3), 528–554.
Glymour, C., & Eberhardt, F. (2012). Hans Reichenbach. In E. N. Zahlta (Ed.), Stanford encyclopedia of philosophy. Retrieved, from http://plato.stanford.edu/archives/win2012/entries/reichenbach/.
Glymour, C. (1980). Theory and evidence. Princeton: Princeton University Press.
Glymour, C. (2012). On the possibility of inference to the best explanation. Journal of Philosophical Logic, 41, 461–469.
Glymour, C., Scheines, R., Spirtes, P., & Kelly, K. (1987). Discovering causal structure. Cambridge: Academic Press.
Hagmayer, Y. (2015). Causal Bayes nets as psychological theories of causal reasoning—Evidence from psychological research. Synthese. doi:10.1007/s11229-015-0734-0.
Hagmayer, Y., & Sloman, S. A. (2009). Decision makers conceive of their choices as interventions. Journal of Experimental Psychology: General, 138(1), 22–38.
Hashem, A. l., & Cooper, G. F. (1996). Human causal discovery from observational data. In Proceedings of the AMIA annual symposium (pp. 27–31). Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2233172/.
Hiddleston, E. (2005). A causal theory of counterfactuals. Noûs, 39(4), 632–657.
Hitchcock, C. R. (2015). Conditioning, intervening, and decision. Synthese. doi:10.1007/s11229-015-0710-8.
Hitchcock, C. (1996). Causal decision theory and decision-theoretic causation. Nous, 30(4), 508–526.
Hoffmann-Kolss, V. (2015). Of brains and planets: On a causal criterion for mind-brain identities. Synthese. doi:10.1007/s11229-015-0671-y.
Jeffrey, R. (1990). The logic of decision. Chicago: University of Chicago Press.
Jeffrey, R. (2004). Subjective probability: The real thing. Cambridge: Cambridge University Press.
Joyce, J. M. (1999). The foundations of causal decision theory. Cambridge: Cambridge University Press.
Kelly, T. K., & Mayo-Wilson, C. (2010). Causal conclusions that flip repeatedly and their justification. In Proceedings of the 26th conference on uncertainty and artificial intelligence (pp. 277–286). Corvallis: AUAI Press.
Kelly, K., Genin, K., & Lin, H. (2015). Realism, rhetoric and reliability. Synthese. doi:10.1007/s11229-015-0993-9.
Lagnado, D. A., & Sloman, S. (2004). The advantage of timely intervention. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30(4), 856–876.
Leamer, E. (1976). Specification searches. New York: Wiley.
Lewis, D. (1973). Causation. Journal of Philosophy, 70, 556–567.
Lewis, D. (1981). Causal decision theory. Australasian Journal of Philosophy, 59(1), 5–30.
Näger, P. (2015). The causal problem of entanglement. Synthese. doi:10.1007/s11229-015-0668-6.
Pearl, J. (1988). Probabilistic reasoning in intelligent systems. San Francisco, CA: Morgan Kaufmann.
Price, H. (1986). Against causal decision theory. Synthese, 67(2), 195–212.
Quesada-Molina, J., Rodriguez-Lallina, J., & Ubeda-Flores, M. (2003). What are copulas? Monographias del Seminario Mathematico Garcia Galdeano, 27, 499–506.
Rosenhouse, J. (2009). The Monty Hall problem. Oxford: Oxford University Press.
Scanlon, T. (2000). What we owe to each other. Cambridge, MA: Belknap Press of Harvard University Press.
Scanlon, T. M. (2014). Being realistic about reasons. Oxford: Oxford University Press.
Schurz, G., & Gebharter, A. (2014). Causality as a theoretical concept: Explanatory warrant and empirical content of the theory of causal nets. Synthese. doi:10.1007/s11229-014-0630-z.
Skyrms, B. (1990). The dynamics of rational deliberation. Cambridge: Harvard University Press.
Sobel, D. M., & Kushnir, T. (2003). Interventions do not solely benefit causal learning: Being told what to do results in worse learning than doing it yourself. In Proceedings of the 25th annual meeting of the Cognitive Science Society.
Sparks, D. L. (1986). Translation of sensory signals into commands for control of saccadic eye movements: Role of primate superior colliculus. Physiological Reviews, 66, 118–171.
Spirtes, P., Glymour, C., Scheines, R., Meek, C., Fienberg, S., & Slate, E. (1992). Prediction and experimental design with graphical causal models. Repository.CMU.Edu.
Spirtes, P., Glymour, C., & Scheines, R. (1993). Causation, prediction, and search (1st ed.). Berlin: Springer.
Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search (2nd ed.). Cambridge: MIT Press.
Steyvers, M., Tenenbaum, J. B., Wagenmakers, E.-J., & Blum, B. (2003). Inferring causal networks from observations and interventions. Cognitive Science, 27, 453–489.
Wenmackers, S., & Romeijn, J.-W. (2014). New theory about old evidence: A framework for open-minded Bayesianism. Synthese. doi:10.1007/s11229-014-0632-x.
Woodward, J. (2015). The problem of variable choice. Synthese. doi:10.1007/s11229-015-0810-5.
Zhang, J., & Spirtes, P. (2015). The three faces of faithfulness. Synthese. doi:10.1007/s11229-015-0673-9.
Zhang, J. (2012). A comparison of three Occam’s razors for Markovian causal models. British Journal for the Philosophy of Science, 64(2), 423–448.
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Glymour, C. Clark Glymour’s responses to the contributions to the Synthese special issue “Causation, probability, and truth: the philosophy of Clark Glymour”. Synthese 193, 1251–1285 (2016). https://doi.org/10.1007/s11229-016-1021-4
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DOI: https://doi.org/10.1007/s11229-016-1021-4