Abstract
In this paper I argue that constitutive relevance relations in mechanisms behave like a special kind of causal relation in at least one important respect: Under suitable circumstances constitutive relevance relations produce the Markov factorization. Based on this observation one may wonder whether standard methods for causal discovery could be fruitfully applied to uncover constitutive relevance relations. This paper is intended as a first step into this new area of philosophical research. I investigate to what extent the PC algorithm, originally developed for causal search, can be used for constitutive relevance discovery. I also discuss possible objections and certain limitations of a constitutive relevance discovery procedure based on PC.
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Notes
Note that there are several possible ways that probability distributions over sets of variables representing mechanisms can obtain. There might, for example, be systems whose constituent variables do not change their values over time. In that case one gets probability distributions by looking at the different values variables take in spatiotemporally different systems of similar type. An example would be probability distributions over mineral components of rocks (cf. Ramsey et al. 2002). The other possibility is that constituent variables change their values over time in one system (see, e.g., Chu et al. 2003). I would like to thank an anonymous referee for pointing this out to me.
A graph is acyclic if it does not feature a path of the form \(X\longrightarrow \cdots \longrightarrow X\).
I am indebted to an anonymous referee for pushing me to think about this issue.
Here is a simple example: Let V be \(\{X,Y,Z\}\). Our system’s causal structure is \(X\longrightarrow Y\longrightarrow Z\). CMC demands that X and Z are screened off by the intermediate cause Y. But now assume that Y and Z have a common cause C that is not captured by V. In that case conditionalizing on Y will activate the path \(X\,\longrightarrow\,Y\,\longleftarrow\,C\longrightarrow Z\,\) and X and Z might still be dependent when conditionalizing on Y. In that case, our model cannot account for the dependence of X on Z given Y and CMC would be violated.
References
Baumgartner, M., & Casini, L. (in press). 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., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36, 421–441.
Chalupka, K., Perona, P., & Eberhardt, F. (2014). Visual causal feature learning. arXiv.org,2309.
Chu, T. J., Glymour, C., Schemes, R., & Spirtes, P. (2003). A statistical problem for inference to regulatory structure from associations of gene expression measurements with microarrays. Bioinformatics, 19(9), 1147–1152.
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.
Eronen, M. I. (2011). Reduction in philosophy of mind. Heusenstamm: De Gruyter.
Fazekas, P., & Kertesz, G. (2011). Causation at different levels: Tracking the commitments of mechanistic explanations. Biology and Philosophy, 26(3), 365–383.
Gebharter, A. (in press). Causal nets, interventionism, and mechanisms: Philosophical foundations and applications. Synthese Library. Dordrecht: Springer.
Gebharter, A. (2015). Causal exclusion and causal Bayes nets. Philosophy and Phenomenological Research. doi:10.1111/phpr.12247.
Glennan, S. (1996). Mechanisms and the nature of causation. Erkenntnis, 44(1), 49–71.
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.
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.
Illari, P. M., Russo, F., & Williamson, J. (Eds.). (2011). Causality in the sciences. Oxford: Oxford University Press.
Kistler, M. (2009). Mechanisms and downward causation. Philosophical Psychology, 22(5), 595–609.
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.
Machamer, P., Darden, L., & Craver, C. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25.
Pearl, J. (2000). Causality (1st ed.). Cambridge: Cambridge University Press.
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. (1956). The direction of time. Berkeley: University of California.
Richardson, T. (1996). A discovery algorithm for directed cyclic graphs. In Uai’96 (pp. 454–461). San Francisco, CA: Morgan Kaufmann.
Romero, F. (2015). Why there isn’t inter-level causation in mechanisms. Synthese, 192(11), 3731–3755.
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.
Silva, R., Scheines, R., Glymour, C, & Spirtes, P. (2006). Learning the structure of linear latent variable models. Journal of Machine Learning Research, 7, 191–246.
Soom, P. (2012). Mechanisms, determination and the metaphysics of neuro-science. 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., Glymour, C., & Schemes, R. (2000). Causation, prediction, and search (2nd ed.). Cambridge: MIT Press.
Woodward, J. (2003). Making things happen. Oxford: Oxford University Press.
Woodward, J. (2015). Interventionism and causal exclusion. Philosophy and Phenomenological Research, 91(2), 303–347.
Zhang, J., & Spirtes, P. (2008). Detection of unfaithfulness and robust causal inference. Minds and Machines, 18(2), 239–271.
Acknowledgements
This work was supported by Deutsche Forschungsgemeinschaft (DFG), research unit FOR 1063. My thanks go to Michael Baumgartner, Lorenzo Casini, Jens Harbecke, Beate Krickel, Gerhard Schurz, and Jon Williamson for important discussions. Thanks also to an anonymous referee for helpful comments.
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Gebharter, A. Uncovering constitutive relevance relations in mechanisms. Philos Stud 174, 2645–2666 (2017). https://doi.org/10.1007/s11098-016-0803-3
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DOI: https://doi.org/10.1007/s11098-016-0803-3