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.

Articles (peer-reviewed)

 

M. Baumgartner and C. Falk (2021), Configurational Causal Modeling and Logic Regression, Multivariate Behavioral Research. [penultimate draft]; [replication material]

M. Baumgartner (2021), Qualitative Comparative Analysis and Robust Sufficiency, Quality & Quantity, doi: 10.1007/s11135-021-01157-z [penultimate draft]

M. Baumgartner and M. Ambühl (2021), Optimizing Consistency and Coverage in Configurational Causal Modeling, Sociological Methods & Research. Replication material. doi: 10.1177/0049124121995554. 

V.P. Parkkinen and M. Baumgartner (2021), Robustness and Model Selection in Configurational Causal Modeling, Sociological Methods & Research. doi: 10.1177/0049124120986200. 

L. Casini and M. Baumgartner (2021), The PC Algorithm and the Inference to Constitution, The British Journal for the Philosophy of Science. doi: 10.1086/714820. Online supplementary material; [penultimate draft]

R.G. Whitaker, N. Sperber, M. Baumgartner, et al. (2020) Coincidence analysis: a new method for causal inference in implementation science, Implementation Science 15, 108 (2020). https://doi.org/10.1186/s13012-020-01070-3

M. Baumgartner and M. Ambühl (2020), Causal Modeling with Multi-Value and Fuzzy-Set Coincidence Analysis, Political Science Research and Methods 8, 526-542, doi: 10.1017/psrm.2018.45. Replication material at https://doi.org/10.7910/DVN/YIEAF1; [penultimate draft]

M. Baumgartner and A. Thiem (2020), Often Trusted But Never (Properly) Tested: Evaluating Qualitative Comparative Analysis, Sociological Methods & Research 49, 279–311, doi: 10.1177/0049124117701487.

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, 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.

M. Baumgartner and L. Casini (2017), An Abductive Theory of Constitution, Philosophy of Science 84, 214-233, doi: 10.1086/690716 [penultimate draft]

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 (2015), Parsimony and Causality, Quality & Quantity 49, 839-856. [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 and I. Drouet (2013), Identifying Intervention Variables, European Journal for Philosophy of Science 3, 183-205. [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.

M. Baumgartner (2012), The Logical Form of Interventionism, Philosophia 40, 751-761. [penultimate draft]

U. Hofmann and M. Baumgartner (2011), Determinism and the Method of Difference, Theoria 26, 155-176.

M. Baumgartner (2010), Shallow Analysis and the Slingshot Argument, Journal of Philosophical Logic 39, 531-556. [penultimate draft]

M. Baumgartner (2010), Interventionism and Epiphenomenalism, Canadian Journal of Philosophy 40, 359-383. [penultimate draft]

T. Lampert and M. Baumgartner (2010), The Problem of Validity Proofs, Grazer Philosophische Studien 80, 79-109. [penultimate draft]

M. Baumgartner (2010), Causal Slingshots, Erkenntnis 72, 111-133. [penultimate draft]

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]

Articles (invited)

M. Baumgartner (2020), Causation, in: The SAGE Handbook of Political Science, ed. by D. Berg-Schlosser, B. Badie, and L. Morlino, London: SAGE, pp. 305–321.

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.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

Software

V.P. Parkkinen and M. Baumgartner (2022). frscore: Functions for Calculating Fit-Robustness of CNA-Solutions. R package version 0.1.1. URL: http://cran.r-project.org/package=frscore

M. Ambuehl and M. Baumgartner. (2021), cnaOpt: Optimizing Consistency and Coverage in Configurational Causal Modeling. R package version 0.5.0. URL: http://cran.r-project.org/package=cnaOpt.

M. Ambuehl and M. Baumgartner. (2021), cna: Causal Modeling with Coincidence Analysis. R package version 3.3.0. URL: http://cran.r-project.org/package=cna.

 

Monographs

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.

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 (2010), Measuring and Governing, Review of "The Law-Governed Universe" by John T. Roberts, Metascience. [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.

 

2024

• Baumgartner, Michael Clemens; Falk, Christoph (2024). Quantifying the quality of configurational causal models. (external link)
• A. Taylor, Kelley; Michael A., Incze; Baumgartner, Michael Clemens et al. (2024). Predictors of urine toxicology and other biologic specimen missingness in randomized trials of substance use disorders. (external link)
• Swiatczak, Martyna; Baumgartner, Michael Clemens (2024). Data Imbalances in Coincidence Analysis: A Simulation Study. (external link)

2023

• Parkkinen, Veli Pekka Kalevi; Baumgartner, Michael Clemens (2023). frscore: Functions for Calculating Fit-Robustness of CNA-Solutions, Version 0.3.1. (external link)
• Baumgartner, Michael Clemens; Ambühl, Matthias (2023). cna: Causal Modeling with Coincidence Analysis, R-version 3.5.1. (external link)

2022

• Baumgartner, Michael Clemens; Mathias, Ambühl (2022). cnaOpt: Optimizing Consistency and Coverage in Configurational Causal Modeling, Version 0.5.2. (external link)

2021

• Baumgartner, Michael Clemens (2021). Qualitative Comparative Analysis and robust sufficiency. (external link)
• Parkkinen, Veli Pekka Kalevi; Baumgartner, Michael Clemens (2021). Robustness and Model Selection in Configurational Causal Modeling. (external link)
• Casini, Lorenzo; Baumgartner, Michael Clemens (2021). The PC Algorithm and the Inference to Constitution. (external link)
• Baumgartner, Michael Clemens; Falk, Christoph (2021). Configurational Causal Modeling and Logic Regression. (external link)
• Baumgartner, Michael Clemens; Ambühl, Mathias (2021). Optimizing Consistency and Coverage in Configurational Causal Modeling. (external link)

2020

• Whitaker, Rebecca; Sperber, Nina; Baumgartner, Michael Clemens et al. (2020). Coincidence analysis: a new method for causal inference in implementation science. (external link)
• Baumgartner, Michael Clemens; Thiem, Alrik (2020). Often Trusted but Never (Properly) Tested: Evaluating Qualitative Comparative Analysis. (external link)
• Baumgartner, Michael Clemens (2020). Causation. (external link)

2019

• Baumgartner, Michael Clemens; Ambühl, Mathias (2019). Optimizing Consistency and Coverage in Configurational Causal Modeling. (external link)
• Baumgartner, Michael Clemens; Falk, Christoph (2019). Boolean Difference-Making: A Modern Regularity Theory of Causation. (external link)

2018

• Baumgartner, Michael Clemens (2018). The Inherent Empirical Underdetermination of Mental Causation. (external link)
• Baumgartner, Michael Clemens (2018). cna: Causal Modeling with Coincidence Analysis, R-version 2.1.0. (external link)
• Baumgartner, Michael Clemens; Casini, Lorenzo; Krickel, Beate (2018). Horizontal Surgicality and Mechanistic Constitution. (external link)
• Baumgartner, Michael Clemens; Ambühl, Mathias (2018). Causal modeling with multi-value and fuzzy-set Coincidence Analysis. (external link)

2017

• Baumgartner, Michael Clemens; Wilutzky, Wendy (2017). Is it possible to experimentally determine the extension of cognition?. (external link)

See a complete overview of publications in Cristin.

Research Council of Norway (FRIPRO): Advancing Causal Modeling with Coincidence Analysis (AdCNA)

Background. Coincidence Analysis (CNA) is a configurational comparative method of causal data analysis that was first introduced in (Baumgartner 2009a, 2009b), substantively re-worked and generalized in (Baumgartner and Ambühl 2020), and implemented in a software library of the R environment for statistical computing in (Ambühl and Baumgartner 2020). In recent years, CNA was applied in numerous studies in public health as well as in the social and political sciences. For example, Dy et al. (2020) used CNA to investigate how different implementation strategies influence patient safety culture in medical homes. Yakovchenko et al. (2020) applied the method to data on the factors affecting the uptake of innovation in the treatment of hepatitis C virus infection, while Haesebrouck (2019) drew on CNA to search for factors influencing EU member states’ participation in the military operations in Libya and against the Islamic State. In contrast to more standard methods of data analysis, which primarily quantify effect sizes, CNA belongs to a family of methods designed to group causal influence factors conjunctively (i.e. in complex bundles) and disjunctively (i.e. on alternative pathways). It is firmly rooted in a so-called regularity theory of causation and it is the only method of its kind that can process data generated by causal structures with multiple outcomes (effects), for example, causal chains.

Main goals. The development of CNA is not finished. The AdCNA project will address four remaining weaknesses and limitations of the method. First, CNA’s applicability, which is currently limited to data on a maximum of about 15 factors, shall be extended to data of significantly higher dimensionality. Second, we will develop CNA-specific inference tests to further improve the quality of the method’s output—at present, that quality is not high enough when the data have small sample sizes and high noise levels. Third, instruments will be devised for reducing model ambiguities, which are particularly severe when the data are heavily fragmented. Fourth, by applying CNA in studies on auditory hallucinations and infant mortality, we will extend the scope of CNA applications to psychology and epidemiology. Overall, CNA has proven its value in some disciplines. But to establish itself in the methodological toolbox of the special sciences, more algorithmic power and flexibility, more output reliability, and wider dissemination are needed. The AdCNA project sets out to deliver exactly that.

Collaborators on this project:

• Dr. Veli-Pekka Parkkinen
• Dr. Mathias Ambühl
• Luna De Souter

 

Research Project for the Toppforsk-programme: Coincidence Analysis

Since the late 1980ies, configurational comparative methods (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 & Ambuehl (2020) and is available as software package for the R environment (Ambuehl & Baumgartner 2020).

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 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. 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:

• Dr. Veli-Pekka Parkkinen
• Dr. Mathias Ambühl
• Dr. Martyna Swiatczak
• Dr. Christoph Falk

 

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.