Browsing Bergen Open Research Archive by Author "Baumgartner, Michael Clemens"

Now showing items 1-9 of 9

    • Boolean Difference-Making: A Modern Regularity Theory of Causation 

      Baumgartner, Michael Clemens; Falk, Christoph (Peer reviewed; Journal article, 2019)
      A regularity theory of causation analyses type-level causation in terms of Boolean difference-making. The essential ingredient that helps this theoretical framework overcome the problems of Hume’s and Mill’s classical ...

    • Coincidence analysis: a new method for causal inference in implementation science 

      Whitaker, Rebecca; Sperber, Nina; Baumgartner, Michael Clemens; Thiem, Alrik; Cragun, Deborah; Damschroder, Laura; Miech, Edward J.; Slade, Alecia; Birken, Sarah (Journal article; Peer reviewed, 2020)
      Background Implementation of multifaceted interventions typically involves many diverse elements working together in interrelated ways, including intervention components, implementation strategies, and features of local ...

    • Configurational Causal Modeling and Logic Regression 

      Baumgartner, Michael Clemens; Falk, Christoph (Journal article; Peer reviewed, 2021)
      Configurational comparative methods (CCMs) and logic regression methods (LRMs) are two families of exploratory methods that employ very different techniques to analyze data generated by causal structures featuring conjunctural ...

    • Data Imbalances in Coincidence Analysis: A Simulation Study 

      Swiatczak, Martyna; Baumgartner, Michael Clemens (Journal article; Peer reviewed, 2024)
      In this paper, we investigate the conditions under which data imbalances, a common data characteristic that occurs when factor values are unevenly distributed, are problematic for the performance of Coincidence Analysis ...

    • Optimizing Consistency and Coverage in Configurational Causal Modeling 

      Baumgartner, Michael Clemens; Ambühl, Mathias (Journal article; Peer reviewed, 2021)
      Consistency and coverage are two core parameters of model fit used by configurational comparative methods (CCMs) of causal inference. Among causal models that perform equally well in other respects (e.g., robustness or ...

    • The PC Algorithm and the Inference to Constitution 

      Casini, Lorenzo; Baumgartner, Michael Clemens (Journal article; Peer reviewed, 2023)
      Gebharter has proposed using one of the best known Bayesian network causal discovery algorithms, PC, to identify the constitutive dependencies underwriting mechanistic explanations. His proposal assumes that mechanistic ...

    • Qualitative Comparative Analysis and robust sufficiency 

      Baumgartner, Michael Clemens (Journal article; Peer reviewed, 2022)
      Some methodologists take the search target of Qualitative Comparative Analysis (QCA) to be causal INUS-conditions, others contend that QCA should instead be used to search for some form of sufficiency that is more substantive ...

    • Quantifying the quality of configurational causal models 

      Baumgartner, Michael Clemens; Falk, Christoph (Journal article; Peer reviewed, 2024)
      There is a growing number of studies benchmarking the performance of configurational comparative methods (CCMs) of causal data analysis. A core benchmark criterion used in these studies is a dichotomous (i.e., non-quantitative) ...

    • Robustness and Model Selection in Configurational Causal Modeling 

      Parkkinen, Veli Pekka Kalevi; Baumgartner, Michael Clemens (Journal article; Peer reviewed, 2021)
      In recent years, proponents of configurational comparative methods (CCMs) have advanced various dimensions of robustness as instrumental to model selection. But these robustness considerations have not led to computable ...