Causation in Biological Sciences

Jacob P. Neal (University of Pittsburgh)
Philosophers of biology generally agree that causal specificity tracks biological importance: more specific causes are more important. Largely focused on explanatory contexts, this discussion of causal specificity neglects an important aspect of biological practice, namely, intervention. I argue the importance of causal specificity does not hold in much biological research aimed at intervention. Applying Woodward’s (2010) analysis of causal specificity to the development and design of HIV treatments, I show that drugs that are less causally specific produce better therapeutic outcomes and are more highly valued. Thus, I conclude the importance of causal specificity varies depending upon the biological practices one considers.

Wes Anderson (Western New England University)
Philosophers such as Glymour and French, and Okasha aim to causally conceptualize group, neighborhood, multi-level, or kin selection, and thereby reconceptualize the inferential practices regarding this research area. Their applications of causal modeling tools to such cases essentially rely on the causal modeler's standard toolkit. But the use of the standard toolkit here can lead to problems. I, therefore, claim that given causal conceptualizations, if we desire to make use of them in inferential practice then we need new graphical representations and re-characterizations of d-separation.

Derek Skillings (University of Bordeaux, CNRS)
This paper examines two types of difficulties encountered while reasoning about biological systems. The first, which I call the translation problem, is a difficulty with imagining and predicting the behavior of biological phenomena that stems from the stochastic and contingent nature of many biological processes. The second, dubbed the interpretation problem, is a difficulty with identifying all the numerous salient causal factors and interactions behind the production of biological phenomena. Approaches to causal explanation are best seen as heuristics, which focuses our attention on both how our conceptual tools resolve problems, and when and where they can break down.