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.
M. Baumgartner and C. Falk (2019), Boolean Difference-Making: A Modern Regularity Theory of Causation, The British Journal for the Philosophy of Science, doi: 10.1093/bjps/axz047. Online supplementary material. [penultimate draft]
M. Baumgartner and M. Ambühl (2018), Causal Modeling with Multi-Value and Fuzzy-Set Coincidence Analysis, Political Science Research and Methods. Replication material at https://doi.org/10.7910/DVN/YIEAF1; [penultimate draft]
M. Baumgartner, L. Casini, and B. Krickel (2018), Horizontal Surgicality and Mechanistic Constitution, Erkenntnis, doi: 10.1007/s10670-018-0033-5. [penultimate draft]
M. Baumgartner (2018), The Inherent Empirical Underdetermination of Mental Causation, The Australasian Journal of Philosophy96, 335-350, doi: 10.1080/00048402.2017.1328451.
M. Baumgartner and W. Wilutzky (2017), Is It Possible to Experimentally Determine the Extension of Cognition?, Philosophical Psychology30, 1104-1125, doi: 10.1080/09515089.2017.1355453. [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]
M. Baumgartner (forthcoming), Causation, in: Handbook of Political Science – A Global Perspective, ed. by D. Berg-Schlosser, B. Badie, and L. Morlino, SAGE, London.
M. Baumgartner and M. Ambühl (2019), cna: An R Package for Configurational Causal Inference and Modeling. R package vignette: The Comprehensive R Archive Network. https://cran.r-project.org/web/packages/cna/vignettes/cna_vignette.pdf.
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
M. Ambuehl and M. Baumgartner. (2019), cnaOpt: Optimizing Consistency and Coverage in Configurational Causal Modeling. R package version 0.1.0. URL: http://cran.r-project.org/package=cnaOpt.
M. Ambuehl and M. Baumgartner. (2019), cna: Causal Modeling with Coincidence Analysis. R package version 2.2.1. URL: http://cran.r-project.org/package=cna.
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 (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.
- 2020. cna: Causal Modeling with Coincidence Analysis, R-version 3.0.0.
- 2020. Often Trusted but Never (Properly) Tested: Evaluating Qualitative Comparative Analysis. Sociological Methods & Research. 279-311.
- 2020. Coincidence analysis: a new method for causal inference in implementation science. Implementation Science.
- 2020. Causation. 17 pages.
- 2019. Optimizing Consistency and Coverage in Configurational Causal Modeling.
- 2019. Boolean Difference-Making: A Modern Regularity Theory of Causation. British Journal for the Philosophy of Science.
- 2018. cna: Causal Modeling with Coincidence Analysis, R-version 2.1.0.
- 2018. The Inherent Empirical Underdetermination of Mental Causation. Australasian Journal of Philosophy. 335-350.
- 2018. Horizontal Surgicality and Mechanistic Constitution. Erkenntnis: An International Journal of Scientific Philosophy. 14 pages.
- 2018. Causal modeling with multi-value and fuzzy-set Coincidence Analysis. Political Science Research and Methods. 526-542.
- 2017. Is it possible to experimentally determine the extension of cognition? Philosophical Psychology. 1104-1125.
Coincidence Analysis Coincidence Analysis is a member of the family of configurational comparative methods (CCMs) of causal data analysis—also known as set-theoretic or Boolean methods. Since the late 1980ies CCMs have gradually been added to the methodological toolkit in disciplines as diverse as political science, sociology, business administration, management, environmental science, evaluation science, and public health. The most prominent CCM is Qualitative Comparative Analysis (QCA) (Ragin 2008). QCA, however, is unsuited to analyze causal structures with more than one endogenous variable, e.g. structures with common causes or causal chains. To overcome that restriction, Coincidence Analysis (CNA) has been first introduced in Baumgartner (2009a, 2009b). It has meanwhile been generalized in Baumgartner & Ambühl (2018) and is available as software package for the R environment (Ambuehl & Baumgartner 2019).
This project has three objectives. The first is to fill all remaining gaps in the methodological protocol of CNA and to complement the CNA R-package accordingly. In particular, tools for robustness tests of CNA models and strategies for reducing model ambiguities shall be developed. The second objective is to systematically test the inferential potential of CNA by applying it to real-life studies from varying disciplines and, thereby, to explore the applicability of CNA outside of the standard domain of CCMs, for example, in biology, medicine or psychology. The third objective is to analyze the relationship between CNA and methods from other theoretical traditions—in particular Bayes-nets methods (cf. Spirtes et al. 2000; Pearl 2000) and regression-analytical methods (Gelman and Hill 2007). Are there substantive points of contact between these methodological traditions? Are there ways to fruitfully integrate them in multi-method studies? What are the conditions that determine what method is best suited to investigate a given phenomenon or to answer a given research question?
Collaborators on this project:
Further Ongoing Projects
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.
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.