Recent and upcoming talks


  • Robustness in configurational causal modelling

    At CLMPST 2019, Prague, August 7, 2019


    This talk presents joint work with Michael Baumgartner. In the talk we describe a notion of robustness for configurational causal models (CCMs) and present simulation results to validate this notion as a tool for model selection, and briefly compare this notion to notions of statistical robustness familiar from regression analytic modeling (RAMs). Where RAMs relate variables and quantify net effects across varying background conditions, CCMs search for dependencies between values of variables, and return models that satisfy the conditions of an INUS-theory of causation.
  • Are model organisms like theoretical models?

    At ISHPSSB 2019, University of Oslo, July 8, 2019


    Models in science are often thought of as surrogates for their intended target systems: one learns about the target indirectly by studying the model and then extrapolating the findings to the target. This analysis is often thought to cover both theoretical models and concrete models systems – simulated economies stand in for actual economies, simple laboratory organisms stand in for more complex organisms and so on. In this talk I argue that model organisms and other concrete models are in an important sense not like theoretical models.
  • How to tell if drugs work

    At Fof research seminar, Bergen, May 2, 2019


    This was a talk at a departmental research seminar at UiB philosophy. According to prevailing evidence-based medicine (EBM) guidelines, randomized controlled trials (RCTs) provide the best evidence for efficacy of medical interventions and when available, trump other types of evidence. Some philosophers argue for mechanistic evidence to be considered alongside RCTs (Russo & Williamson, 2007), while others defend EBM with minor qualifications (Howick, 2011; Howick, Glasziou, & Aronson 2013). Yet others suggest that debating the merits of different types of evidence is irrelevant as long as our theories of evidence and causality idealize away bias in the evidence-base (Holman, 2017; Stegenga, 2018).