Genome-wide association studies of human complex traits have provided us with new estimates of heritability. These estimates foreground the question of genetic causation. After having presen...

Heritability is routinely interpreted causally. Yet, what such an interpretation amounts to is often unclear. Here, I provide a causal interpretation of this concept in terms of range of causal influence, one of several causal dimensions proposed within the interventionist account of causation. An information-theoretic measure of range of causal influence has recently been put forward in the literature. Starting from this formalization and relying upon Woodward’s analysis, I show that an important problem associated with interpreting heritability causally, namely the (...) locality problem, amounts, at least partly, to a low invariance and low stability between the genotype/environment and the phenotype of individuals. In light of this, I plead for a causal interpretation of heritability that takes the notions of Woodward’s invariance and stability into consideration. In doing so, I defuse naive causal interpretations of heritability. (shrink)

The interventionist account provides us with several notions permitting the qualification of causal relationships. In recent years, there has been a push toward formalizing these notions using information theory. In this paper, I discuss one of them, namely causal specificity. The notion of causal specificity is ambiguous as it can refer to at least two different concepts. After having presented these, I show that current attempts to formalize causal specificity in information theoretic terms have mostly focused on one of these (...) two concepts. I then propose and apply a new information-theoretic measure which captures the other concept. (shrink)

Recent work by Brian Skyrms offers a very general way to think about how information flows and evolves in biological networks — from the way monkeys in a troop communicate, to the way cells in a body coordinate their actions. A central feature of his account is a way to formally measure the quantity of information contained in the signals in these networks. In this paper, we argue there is a tension between how Skyrms talks of signalling networks and his (...) formal measure of information. Although Skyrms refers to both how information flows through networks and that signals carry information, we show that his formal measure only captures the latter. We then suggest that to capture the notion of flow in signalling networks, we need to treat them as causal networks. This provides the formal tools to define a measure that does capture flow, and we do so by drawing on recent work defining causal specificity. Finally, we suggest that this new measure is crucial if we wish to explain how evolution creates information. For signals to play a role in explaining their own origins and stability, they can’t just carry information about acts: they must be difference-makers for acts. (shrink)

In recent years the notion of biological specificity has attracted significant philosophical attention. This paper focuses on host specificity, a kind of biological specificity that has not yet been discussed by philosophers, and which concerns the extent to which a species is selective in the range of other species it exploits for feeding and/or reproduction. Host specificity is an important notion in ecology, where it plays a variety of theoretical roles. Here I focus on the role of host specificity in (...) biological control, a field of applied ecology that deals with the suppression of pests through the use of living organisms. Examining host specificity and its role in biological control yields several valuable contributions to our understanding of biological specificity. In particular, I argue that host specificity cannot be fully understood in terms of Woodward’s well-known account of causal specificity. To adequately account for host specificity, we need a notion of causal specificity that takes into consideration the extent to which a variable’s effects are similar to one another – a dimension not captured in Woodward’s account. In addition, the literature on host specificity in biological control highlights certain aspects in which causally specific relationships can be practically valuable that have not yet been addressed in philosophical discussions of specificity. That literature also reveals that in certain contexts specificity can hinder rather than foster effective control, thus leading to a nuanced assessment of the practical value of specific causes. (shrink)

This paper is concerned with the content of the causal proportionality constraint. It investigates two general versions of the constraint, namely “horizontal” and “vertical” proportionality. Moreover, it discusses whether proportionality is considered an ontic or an epistemic, i.e. explanatory, constraint on causation in the context of some of the most prominent theories of causation. The following main claims are defended: (1) The horizontal (HP) and the vertical version (VP) of the proportionality constraint are logically independent. (2) HP is implied by (...) some prominent theories of causation, not by others. (3) None of the discussed popular theories of causation contradicts either HP or VP. (4) HP and VP are not ontic or epistemic principles as such; rather, whether they are ontic or epistemic depends on the theories chosen plus background assumptions about the existence of higher-level causes and their non-identity to lower-level ones. (shrink)

A causal approach to biological information is outlined. There are two aspects to this approach: information as determining a choice between alternative objects and information as determining the construction of a single object. The first aspect has been developed in earlier work to yield a quantitative measure of biological information that can be used to analyze biological networks. This article explores the prospects for a measure based on the second aspect and suggests some applications for such a measure. These two (...) aspects are not suggested to exhaust all the facets of biological information. (shrink)

I use some recent formal work on measuring causation to explore a suggestion by James Woodward: that the notion of causal specificity can clarify the distinction in biology between permissive and instructive causes. This distinction arises when a complex developmental process, such as the formation of an entire body part, can be triggered by a simple switch, such as the presence of particular protein. In such cases, the protein is said to merely induce or "permit" the developmental process, whilst the (...) causal "instructions" for guiding that process are already prefigured within the cells. I construct a novel model that expresses in a simple and tractable way the relevant causal structure of biological development and then use a measure of causal specificity to analyse the model. I show that the permissive-instructive distinction cannot be captured by simply contrasting the specificity of two causes as Woodward proposes, and instead introduce an alternative, hierarchical approach to analysing the interaction between two causes. The resulting analysis highlights the importance of focusing on gene regulation, rather than just the coding regions, when analysing the distinctive causal power of genes. (shrink)

I argue that Calcott :481–505, Calcott 2017) mischaracterizes in an important way the notion of causal specificity proposed by Woodward :287–318, Woodward 2010). This leads him to rely too heavily on one single aspect of Woodward’s analysis on causal specificity; propose an information-theoretic measure he calls ‘precision’ which is partly redundant with, but less general than one of the dimensions in Woodward’s analysis of specificity, without acknowledging Woodward’s analysis; and claim that comparing the specificities of two or more causes under (...) what he calls a competitive analysis of causes, does not permit to capture the distinction between permissive and instructive causes. After having restated Woodward’s analysis of causal specificity, I present an information-theoretic measure which, although related to Calcott’s measure, is more general than his and corresponds to the notion of specificity he missed in Woodward's analysis. I then show how this measure can be used, together with mutual causal information, to distinguish permissive from instructive causes in a competitive analysis of causes. (shrink)

The idea that good explanations come with strong changes in probabilities has been very common. This criterion is called probabilistic difference-making. Since it is an intuitive criterion and has a long tradition in the literature on scientific explanation, it comes as a surprise that probabilistic difference-making is rarely discussed in the context of interventionist causal explanation. Specificity, proportionality, and stability are usually employed to measure explanatory power instead. This paper is a first step into the larger project of connecting difference-making (...) to the interventionist debate, and it starts by investigating whether probabilistic difference-making is contained in the notion of specificity. The choice of specificity is motivated by the observation that both probabilistic difference-making and specificity build on similar underlying intuitions. When comparing measures for both specificity and probabilistic difference-making, it turns out that the measures are not strictly correlated, and so the thesis that probabilistic difference-making is encoded within specificity has to be rejected. Some consequences of this result are discussed as well. (shrink)

Human microbiome research makes causal connections between entire microbial communities and a wide array of traits that range from physiological diseases to psychological states. To evaluate these causal claims, we first examine a well-known single-microbe causal explanation: of Helicobacter pylori causing ulcers. This apparently straightforward causal explanation is not so simple, however. It does not achieve a key explanatory standard in microbiology, of Koch’s postulates, which rely on manipulations of single-microorganism cultures to infer causal relationships to disease. When Koch’s postulates (...) are framed by an interventionist causal framework, it is clearer what the H. pylori explanation achieves and where its explanatory strengths lie. After assessing this ‘simple’, single-microbe case, we apply the interventionist framework to two key areas of microbiome research, in which obesity and mental health states are purportedly explained by microbiomes. Despite the experimental data available, interventionist criteria for explanation show that many of the causal claims generated by microbiome research are weak or misleading. We focus on the stability, specificity and proportionality of proposed microbiome causal explanations, and evaluate how effectively these dimensions of causal explanation are achieved in some promising avenues of research. We suggest some conceptual and explanatory strategies to improve how causal claims about microbiomes are made. (shrink)

There is a tension between, on the one hand, the view that natural selection refers to individual-level causes, and on the other hand, the view that it refers to a population-level cause. In this article, I make the case for the individual-level cause view. I respond to recent claims made by McLoone that the individual-level cause view is inconsistent. I show that if one were to follow his arguments, any causal claim in any context would have to be regarded as (...) vindicating a form of population-level cause view. I show why this is implausible and how a consistent individual-level cause position can be held within the interventionist account of causation. Finally, I argue that there is one sense in which natural selection might be said to refer to population-level causes of evolutionary change. The upshot is that, as noted by others, natural selection can be regarded as referring to a population-level cause in the context of frequency-dependent selection and other situations of fitness-altering interactions between the individuals of a population. But whether this statement is true will depend on the empirical case investigated, not some a priori conceptual distinction. Thus, even though situations of frequency dependence might be ubiquitous, it is orthogonal to the conceptual question of whether frequency-independent natural selection—McLoone’s target—refers to individual- or population-level causes. (shrink)

In this paper I critically evaluate Reisman and Forber’s :1113–1123, 2005) arguments that drift and natural selection are population-level causes of evolution based on what they call the manipulation condition. Although I agree that this condition is an important step for identifying causes for evolutionary change, it is insufficient. Following Woodward, I argue that the invariance of a relationship is another crucial parameter to take into consideration for causal explanations. Starting from Reisman and Forber’s example on drift and after having (...) briefly presented the criterion of invariance, I show that once both the manipulation condition and the criterion of invariance are taken into account, drift, in this example, should better be understood as an individual-level rather than a population-level cause. Later, I concede that it is legitimate to interpret natural selection and drift as population-level causes when they rely on genuinely indeterministic events and some cases of frequency-dependent selection. (shrink)