One of Stuart Glennan's most prominent contributions to the new mechanist debate consists in his reductive analysis of higher-level causation in terms of mechanisms (Glennan, 1996). In this paper I employ the causal Bayes net framework to reconstruct his analysis. This allows for specifying general assumptions which have to be satis ed to get Glennan's approach working. I show that once these assumptions are in place, they imply (against the background of the causal Bayes net machinery) that higher-level causation indeed (...) reduces to interactions between component parts of mechanisms. I also briefly discuss the plausibility of these assumptions and some consequences for the mechanism debate. (shrink)

I examine to what extent accounts of mechanisms based on formal interventionist theories of causality can adequately represent biological mechanisms with complex dynamics. Using a differential equation model for a circadian clock mechanism as an example, I first show that there exists an iterative solution that can be interpreted as a structural causal model. Thus, in principle, it is possible to integrate causal difference-making information with dynamical information. However, the differential equation model itself lacks the right modularity properties for a (...) full integration. A formal mechanistic model will therefore have to leave out either noncausal or causal explanatory relations. (shrink)

Russo and Williamson put forward the following thesis: in order to establish a causal claim in medicine, one normally needs to establish both that the putative cause and putative effect are appropriately correlated and that there is some underlying mechanism that can account for this correlation. I argue that, although the Russo-Williamson thesis conflicts with the tenets of present-day evidence-based medicine, it offers a better causal epistemology than that provided by present-day EBM because it better explains two key aspects of (...) causal discovery. First, the thesis better explains the role of clinical studies in establishing causal claims. Second, it yields a better account of extrapolation. (shrink)

Causal Bayes nets (CBNs) can be used to model causal relationships up to whole mechanisms. Though modelling mechanisms with CBNs comes with many advantages, CBNs might fail to adequately represent some biological mechanisms because—as Kaiser (2016) pointed out—they have problems with capturing relevant spatial and structural information. In this paper we propose a hybrid approach for modelling mechanisms that combines CBNs and cellular automata. Our approach can incorporate spatial and structural information while, at the same time, it comes with all (...) the merits of a CBN representation of mechanisms. (shrink)

Mechanisms play an important role in many sciences when it comes to questions concerning explanation, prediction, and control. Answering such questions in a quantitative way requires a formal represention of mechanisms. Gebharter (2014) suggests to represent mechanisms by means of one or more causal arrows of an acyclic causal net. In this paper we show how this approach can be extended in such a way that it can also be fruitfully applied to mechanisms featuring causal feedback.

In contemporary philosophy of science, the consensus view seems to be that scientific explanations describe mechanisms responsible for the phenomena to be explained. Two kinds of explanatory relevance figure in mechanistic accounts of explanation: causal and constitutive. Following prominent accounts, it seems natural to analyze both these relations in terms of systematic interventions into some factor X with respect to another factor Y. However, such interventions are tailored to uncover causal relations only. Construing the constitutive relationship between parts and wholes (...) in terms of interventions thus raises metaphysical, conceptual, and epistemological questions. We here review the barriers that intervention-based inquiry into mechanisms encounters and consider some solutions. (shrink)