I am a full professor at the Department of Philosophy of the University of Bergen. My PhD is from the University of Bern, Switzerland (2005). I work on questions in the philosophy of science and the philosophy of logic, in particular, on causation, mechanistic constitution, and logical formalization. My publications include an introduction to the philosophy of causation entitled "Kausalitaet und kausales Schliessen" (2004) and numerous papers on causation, causal reasoning, regularity theories, interventionism, mechanistic constitution, non-reductive physicalism, epiphenomenalism, determinism, logical formalization, argument reconstruction/evaluation, modeling in the social sciences, QCA, and the slingshot argument.
1) Version 2.0.0 of the cna R-package is now available on CRAN: https://cran.r-project.org/package=cnaWhile previous versions have only been capable of processing dichotomous variables, version 2.0.0 generalizes cna for multi-value and continuous variables whose values are interpreted as membership scores in fuzzy sets.
2) From September 25-29, 2017, Alrik Thiem and I will offer an intensive one-week introduction to causal modeling with QCA and CNA at the Regenstrief Institute in Indianapolis. For more information and registration click here.
M. Baumgartner and G. Grasshoff, Kausalität und kausales Schliessen. Eine Einführung mit interaktiven Übungen, Bern Studies in the History and Philosophy of Science, Bern, 2004.
M. Baumgartner, Complex Causal Structures. Extensions of a Regularity Theory of Causation, PhD-thesis, University of Bern, 2006.
Special Journal Issue
M. Baumgartner and L. Glynn (eds.), Actual Causation, special issue of Erkenntnis, vol. 78, issue 1 supplement, 2013.
M. Baumgartner and W. Wilutzky (forthcoming), Is It Possible to Experimentally Determine the Extension of Cognition?, Philosophical Psychology. [penultimate draft]
M. Baumgartner (2017), The Inherent Empirical Underdetermination of Mental Causation, The Australasian Journal of Philosophy, doi: 10.1080/00048402.2017.1328451.
M. Baumgartner and A. Thiem (2017), Often Trusted But Never (Properly) Tested: Evaluating Qualitative Comparative Analysis, Sociological Methods & Research, doi: 10.1177/0049124117701487.
A. Thiem and M. Baumgartner (2016), Modeling Causal Irrelevance in Evaluations of Configurational Comparative Methods, Sociological Methodology 46, 345-357, doi: 10.1177/0081175016654736.
A. Thiem and M. Baumgartner (2016), Back to Square One: A Reply to Munck, Paine and Schneider, Comparative Political Studies 49, 801-806.
A. Thiem, M. Baumgartner and D. Bol (2016), Still Lost in Translation! A Correction of Three Misunderstandings Between Configurational Comparativists and Regressional Analysts, Comparative Political Studies 49, 742-774.
M. Baumgartner and A. Gebharter (2016), Constitutive Relevance, Mutual Manipulability, and Fat-Handedness, The British Journal for the Philosophy of Science 67, 731-756. [penultimate draft]
M. Baumgartner and A. Thiem (2015), Model Ambiguities in Configurational Comparative Research, Sociological Methods & Research, doi: 10.1177/0049124115610351.
M. Baumgartner and A. Thiem (2015), Identifying Complex Causal Dependencies in Configurational Data with Coincidence Analysis, The R Journal 7, 176-184. [penultimate draft]
M. Baumgartner (2014), Exhibiting Interpretational and Representational Validity, Synthese 191, 1349-1373. [penultimate draft]
M. Baumgartner and R. Epple (2014), A Coincidence Analysis of a Causal Chain: The Swiss Minaret Vote, Sociological Methods & Research 43, 280-312. [penultimate draft]
M. Baumgartner (2013), A Regularity Theoretic Approach to Actual Causation, Erkenntnis 78, 85-109. [penultimate draft]
M. Baumgartner (2013), Rendering Interventionism and Non-Reductive Physicalism Compatible, Dialectica 67, 1-27. [penultimate draft]
M. Baumgartner (2013), Detecting Causal Chains in Small-N Data, Field Methods 25, 3-24. [penultimate draft]
M. Baumgartner and L. Glynn (2013), Introduction to Special Issue of Erkenntnis on 'Actual Causation', Erkenntnis 78, 1-8.
U. Hofmann and M. Baumgartner (2011), Determinism and the Method of Difference, Theoria 26, 155-176.
M. Baumgartner (2009), Uncovering Deterministic Causal Structures: A Boolean Approach, Synthese 170, 71-96. [Springer Nature SharedIt] [penultimate draft]
M. Baumgartner (2009), Interdefining Causation and Intervention, Dialectica 63, 175-194. [penultimate draft]
M. Baumgartner (2009), Interventionist Causal Exclusion and Non-reductive Physicalism, International Studies in the Philosophy of Science 23, 161-178. [penultimate draft]
M. Baumgartner (2009), Inferring Causal Complexity, Sociological Methods & Research 38, 71-101. [penultimate draft]
M. Baumgartner (2008), Regularity Theories Reassessed, Philosophia 36, 327-354. [penultimate draft]
M. Baumgartner (2008), The Causal Chain Problem, Erkenntnis 69, 201-226. [Springer Nature SharedIt] [penultimate draft]
M. Baumgartner and T. Lampert (2008), Adequate Formalization, Synthese 164, 93-115. [penultimate draft]
A. Thiem and M. Baumgartner (2016), Glossary for Configurational Comparative Methods, Version 1.0. In: Thiem, Alrik. QCApro: Professional Functionality for Performing and Evaluating Qualitative Comparative Analysis, R Package Version 1.1-0. URL: http://cran.r-project.org/package=QCApro.
M. Baumgartner (2010), Informal Reasoning and Logical Formalization, in: Ding und Begriff, ed. by S. Conrad and S. Imhof, Ontos Verlag, Frankfurt.
M. Baumgartner (2007), Probleme einer theoretischen Analyse der Kausalrelation, in: N. Kersten u. U. Rose, Kausales Schliessen auf der Grundlage von Beobachtungsstudien, BAUA, Dortmund, 16-34.
M. Baumgartner (2006), 'Kausalität', in: Neues Handbuch philosophischer Grundbegriffe, hg. v. Armin G. Wildfeuer und Petra Kolmer, Karl Alber.Software
Ambuehl, Mathias, Michael Baumgartner. 2017. cna: Causal Modeling with Coincidence Analysis. R package version 2.0.0. URL: http://cran.r-project.org/package=cna.Book Reviews
M. Baumgartner (2017), Reasons Without Argument. Review of "Reasons Why" by Bradford Skow, Metascience. [Springer Nature SharedIt]
M. Baumgartner (2017), Review of "Ockham's Razors. A User's Manual" by Elliott Sober, The Australasian Journal of Philosophy. [penultimate draft]
M. Baumgartner and T. Lampert (2004), ‘Die richtige Formel. Philosophische Probleme der logischen Formalisierung’ of G. Brun, Erkenntnis 60.3, pp. 417-421.
Coincidence Analysis Coincidence Analysis (CNA) is a Boolean method of causal data analysis first presented in Baumgartner (2009a; 2009b). CNA has been developed on the methodological drawing board and against the background of various idealizing assumptions. As of now, its applicability to real-life data is hence severely restricted. Moreover, in its current state it can only model dichotomous variables. Nonetheless, as shown in Baumgartner (2014), CNA has some distinguished advantages over the currently dominant approach to Boolean causal discovery, viz. Qualitative Comparative Analysis (QCA) (cf. Ragin 1987; 2008). Contrary to QCA, CNA can process data that are generated by causal chains and common cause structures, it can eliminate all redundancies from causal models without having to resort to counterfactual simplifying assumptions, and CNA searches for causal structures without the analyzed variable set antecedently being partitioned into exogenous and endogenous variables.In light of these promising prospects, this project aims to bring Coincidence Analysis from the drawing board to effective, flexible, and computer-assisted applicability in real-life contexts of causal discovery. In collaboration with researchers working with Boolean causal models—e.g. from the fields of social and political sciences or biology—, CNA shall be adapted to the demands of its users. Furthermore, the theoretical and conceptual foundation of Boolean causal reasoning shall be clarified by spelling out the details of the theory of causation relative to which Boolean models must be interpreted, by analyzing the required background assumptions of Boolean data analysis, by scrutinizing the divide between Boolean and non-Boolean dimensions of causal structures, and by comparing CNA with non-Boolean methods. In this vein, a precise understanding of the domain of applicability and the inferential potential of CNA shall be gained. The output of the project will be a fully worked out, ready-to-use, maximally general, and theoretically grounded method of Boolean causal data analysis that is applicable to data not processable by other methods and that, thus, constitutes a valid alternative for researchers interested in Boolean dimensions of causality.Collaborators on this project:
Further Ongoing Projects
The Extension of Cognition This projects investigates the implications of the currently flourishing theories of mechanistic constitution (e.g. Craver 2007) for the hypothesis of extended cognition, which states that cognitive processes can and do have constituents that occur outside of the head (Clark & Chalmers 1998; Rowlands 2009; Wheeler 2010; Drayson 2010). Collaborator: Wendy Wilutzky.
An Abductive Theory of Constitution The starting point of this project is the recent result of Baumgartner & Gebharter (2015) showing that Craver's popular mutual manipulability account of constitution is unsuited to ground a viable method for the empirical identification of constitutional relations. As an alternative, we develop an abductive theory of constitution, which exploits the fact that phenomena and their constituents are unbreakably coupled via common causes. The best explanation for this common-cause coupling is the existence of an additional dependence relation, viz. constitution. Collaborator: Lorenzo Casini.
A Bayesian Theory of Constitution The goal of this project is to develop a Bayesian theory of constitution that identifies as constituents those spatiotemporal parts of a phenomenon whose causal roles contain the phenomenon's causal role. By drawing on the conceptual resources of Bayesian networks, the project should pave the way for a Bayesian methodology for constitutional discovery. Collaborator: Lorenzo Casini.
Model Ambiguities in QCA This project explores the problem of model ambiguities in Qualitative Comparative Analysis (QCA). Data analyzed by QCA can often be accounted for by multiple causal models that fare equally well with respect to all parameters of fit. The degree of ambiguity sometimes reaches such extreme proportions that no causal conclusions are possible. Yet, due to severe deficiencies in popular QCA software, researchers are typically unaware of these ambiguities. Collaborator: Alrik Thiem.
Evaluating Configurational Comparative Methods To date, hundreds of researchers have employed Qualitative Comparative Analysis (QCA) as a configurational method of empirical social research. However, the correctness of QCA qua causal inference procedure has not been carefully scrutinized in the literature so far. This projects aims to fill that glaring gap. We first lay out the criteria an adequate method of configurational data analysis has to satisfy, and second, implement a battery of inverse-search trials to test how QCA performs with respect to these criteria in different discovery contexts. Collaborator: Alrik Thiem.
Is it Possible to Generate Empirical Evidence for the Existence of Macro-To-Micro Causation? In recent years, numerous non-reductive physicalists (e.g. Shapiro, Sober, Raatikainen, Menzies) have argued that, by adopting a variant of Woodward's (2003) popular interventionist theory of causation, it becomes possible to provide empirical evidence in favor of the existence of macro-to-micro downward causation. This projects intends to show that all of these proposals are bound to fail, for it is impossible, in principle, to generate evidence for downward causation. The question as to the existence of macro-to-micro causation is of inherently pragmatic nature.