Uncovering constitutive relevance relations in mechanisms
- 244 Downloads
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.
KeywordsConstitutive relevance Mechanisms Causal Bayes nets Discovery Causal modeling
- Baumgartner, M., & Casini, L. (in press). An abductive theory of constitution. Philosophy of Science.Google Scholar
- Chalupka, K., Perona, P., & Eberhardt, F. (2014). Visual causal feature learning. arXiv.org,2309.
- Gebharter, A. (in press). Causal nets, interventionism, and mechanisms: Philosophical foundations and applications. Synthese Library. Dordrecht: Springer.Google Scholar
- Gebharter, A. (2015). Causal exclusion and causal Bayes nets. Philosophy and Phenomenological Research. doi:10.1111/phpr.12247.
- Glymour, C., Spirtes, P., & Scheines, R. (1991). Causal inference. Erkenntnis, 35(1/3), 151–189.Google Scholar
- Illari, P. M., Russo, F., & Williamson, J. (Eds.). (2011). Causality in the sciences. Oxford: Oxford University Press.Google Scholar
- Pearl, J. (2000). Causality (1st ed.). Cambridge: Cambridge University Press.Google Scholar
- Reichenbach, H. (1956). The direction of time. Berkeley: University of California.Google Scholar
- Richardson, T. (1996). A discovery algorithm for directed cyclic graphs. In Uai’96 (pp. 454–461). San Francisco, CA: Morgan Kaufmann.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- Spirtes, P., Glymour, C., & Schemes, R. (2000). Causation, prediction, and search (2nd ed.). Cambridge: MIT Press.Google Scholar
- Woodward, J. (2003). Making things happen. Oxford: Oxford University Press.Google Scholar