Causal Modeling in Multilevel Settings: A New Proposal

Jan 1, 2024·
Thomas Blanchard
Thomas Blanchard
,
Andreas Hüttemann
· 0 min read
Abstract
An important question for the causal modeling approach is how to integrate non-causal dependence relations such as asymmetric supervenience into the approach. The most prominent proposal to that effect (due to Gebharter) is to treat those dependence relationships as formally analogous to causal relationships. We argue that this proposal neglects some crucial differences between causal and non-causal dependencies, and that in the context of causal modeling non-causal dependence relationships should be represented as mutual dependence relationships. We develop a new kind of model – ‘hybrid models’ - based on this suggestion, and formulate a set of axioms for those models. Our formalism has important implications for Kim’s exclusion problem: whereas Gebharter’s framework vindicates Kim’s causal exclusion objection against nonreductive physicalism, our framework has no such implication, and can help non-reductive physicalists vindicate the efficacy of high-level properties. A further benefit of our formalism is that it yields a natural and plausible way of thinking about interventions in multi-level contexts.
Type
Publication
Philosophy and Phenomenological Research, 109(2), 433-457
publication
Thomas Blanchard
Authors
Maître de Conférences en Philosophie

I am Associate Professor in the philosophy department at the Université Bordeaux Montaigne. Previously, I was a postdoctoral researcher in philosophy and psychology in the Concepts and Cognition Lab at UC-Berkeley for the Varieties of Understanding Project, an Assistant Professor at Illinois Wesleyan University, and an Akademischer Rat (roughly equivalent to assistant professor) at the University of Cologne. I received my Ph.D. from Rutgers University in 2014.

My research is in the philosophy of science, and focuses mainly on causation, causal modeling and causal explanation. I am interested in a wide variety of issues concerning causation including causal asymmetries, levels of causal explanation, the causal exclusion problem, the epistemology of causal inference, causal cognition, and causal decision theory. My work also investigates the use of certain causal concepts and assumptions in particular sciences such as biology, epidemiology and medicine.