Several authors have argued that causes differ in the degree to which they are ‘specific’ to their effects. Woodward has used this idea to enrich his influential interventionist theory of causal explanation. Here we propose a way to measure causal specificity using tools from information theory. We show that the specificity of a causal variable is not well-defined without a probability distribution over the states of that variable. We demonstrate the tractability and interest of our (...) proposed measure by measuring the specificity of coding DNA and other factors in a simple model of the production of mRNA. (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)

Philosophers of biology generally agree that causal specificity tracks biological importance: more specific causes are more important. I argue that this correlation does not hold in much research aimed at biomedical intervention. Applying James Woodward’s 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 that the importance of biological causes does not track (...) their specificity but instead varies depending on the biological practice we consider and the goals of that practice. (shrink)

Several authors have used the notion of causal specificity in order to defend non-parity about genetic causes (Waters 2007, Woodward 2010, Weber 2017, forthcoming). Non-parity in this context is the idea that DNA and some other biomolecules that are often described as information-bearers by biologists play a unique role in life processes, an idea that has been challenged by Developmental Systems Theory (e.g., Oyama 2000). Indeed, it has proven to be quite difficult to state clearly what the alleged (...) special role of genetic causes consists in. In this paper, I show that the set of biomolecules that are normally considered to be information-bearers (DNA, mRNA) can be shown to be the most specific causes of protein primary structure, provided that causal specificity is measured over a relevant space of biological possibilities, disregarding physical as well as logically possible states of the causal variables. (shrink)

Griffiths et al. (2015) have proposed a quantitative measure of causal specificity and used it to assess various attempts to single out genetic causes as being causally more specific than other cellular mechanisms, for example, alternative splicing. Focusing in particular on developmental processes, they have identified a number of important challenges for this project. In this discussion note, I would like to show how these challenges can be met.

The concept of causal specificity is drawing considerable attention from philosophers of biology. It became the rationale for rejecting (and occasionally, accepting) a thesis of causal parity of developmental factors. This literature assumes that attributing specificity to causal relations is at least in principle a straightforward (if not systematic) task. However, the parity debate in philosophy of biology seems to be stuck at a point where it is not the biological details that will help move (...) forward. In this paper, I take a step back to reexamine the very idea of causal specificity and its intended role in the parity dispute in philosophy of biology. I contend that the idea of causal specificity across variations as currently discussed in the literature is irreducibly twofold in nature: it is about two independent components that are not mutually entailed. I show this to be the source of prior complications with the notion of specificity itself that ultimately affect the purposes for which it is often invoked, notably to settle the parity dispute. (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)

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)

I present a reconstruction of F.H.C. Crick's two 1957 hypotheses "Sequence Hypothesis" and "Central Dogma" in terms of a contemporary philosophical theory of causation. Analyzing in particular the experimental evidence that Crick cited, I argue that these hypotheses can be understood as claims about the actual difference-making cause in protein synthesis. As these hypotheses are only true if restricted to certain nucleic acids in certain organisms, I then examine the concept of causal specificity and its potential to counter (...) claims about causal parity of DNA and other cellular components. I first show that causal specificity is a special kind of invariance under interventions, namely invariance of generalizations that range over finite sets of discrete variables. Then, I show that this notion allows the articulation of a middle ground in the debate over causal parity. (shrink)

Griffiths et al. have proposed a quantitative measure of causal specificity and used it to assess various attempts to single out genetic causes as being causally more specific than other cellular mechanisms, for example, alternative splicing. Focusing in particular on developmental processes, they have identified a number of important challenges for this project. In this discussion note, I would like to show how these challenges can be met.

Purpose The purpose of this paper is to suggest a definition of genetic information by taking into account the debate surrounding it. Particularly, the objections raised by Developmental Systems Theory to Teleosemantic endorsements of the notion of genetic information as well as deflationist approaches which suggest to ascribe the notion of genetic information a heuristic value at most, and to reduce it to that of causality. Design/methodology/approach The paper presents the notion of genetic information through its historical evolution and analyses (...) it with the conceptual tools offered by philosophical theories of causation on one side and linguistics on the other. Findings The concept of genetic information is defined as a special kind of cause which causes something to be one way rather than another, by combining elementary units one way rather than another. Tested against the notion of “genetic error” this definition demonstrates to provide an exhaustive account of the common denominators associated with the notion of genetic information: causal specificity; combinatorial mechanism; arbitrariness. Originality/value The definition clarifies how the notion of information is understood when applied to genetic phenomena and also contributes to the debate on the notion of information, broadly meant, which is still affected by lack of consensus. (shrink)

Binding specificity is a centrally important concept in molecular biology, yet it has received little philosophical attention. Here I aim to remedy this by analyzing binding specificity as a causal property. I focus on the concept’s role in drug design, where it is highly prized and hence directly studied. From a causal perspective, understanding why binding specificity is a valuable property of drugs contributes to an understanding of causal selection—of how and why scientists distinguish (...) between causes, not just causes from noncauses. In particular, the specificity of drugs is precisely what underwrites their value as experimental interventions on biological processes. (shrink)

In this chapter we examine the relationship between biological information, the key biological concept of specificity, and recent philosophical work on causation. We begin by showing how talk of information in the molecular biosciences grew out of efforts to understand the sources of biological specificity. We then introduce the idea of ‘causal specificity’ from recent work on causation in philosophy, and our own, information theoretic measure of causal specificity. Biological specificity, we argue, is (...) simple the causal specificity of certain biological processes. This, we suggest, means that causal relationships in biology are ‘informational’ relationships simply when they are highly specific relationships. Biological information can be identified with the storage, transmission and exercise of biological specificity. It has been argued that causal relationships should not be regarded as informational relationship unless they are ‘arbitrary’. We argue that, whilst arbitrariness is an important feature of many causal relationships in living systems, it should not be used in this way to delimit biological information. Finally, we argue that biological specificity, and hence biological information, is not confined to nucleic acids but distributed among a wide range of entities and processes. (shrink)

In this paper I analyze the process by which modelers in systems biology arrive at an adequate representation of the biological structures thought to underlie data gathered from high-throughput experiments. Contrary to views that causal claims and explanations are rare in systems biology, I argue that in many studies of gene regulatory networks modelers aim at a representation of causal structure. In addressing modeling challenges, they draw on assumptions informed by theory and pragmatic considerations in a manner that (...) is guided by an interventionist conception of causal structure. While doubts have been raised about the applicability of this notion of causality to complex biological systems, it is here seen to be an adequate guide to inquiry. (shrink)

Lynch et al. (Biol Philos 34:62, 2019) employ stability, specificity, and proportionality as criteria for evaluating microbiome causal explanations. Although these causal characteristics signify relevant differences between causal roles, I suggest that they should not be used as general criteria for strong or good causal explanations.

Causal selection is the task of picking out, from a field of known causally relevant factors, some factors as elements of an explanation. The Causal Parity Thesis in the philosophy of biology challenges the usual ways of making such selections among different causes operating in a developing organism. The main target of this thesis is usually gene centrism, the doctrine that genes play some special role in ontogeny, which is often described in terms of information-bearing or programming. This (...) paper is concerned with the attempt of confronting the challenge coming from the Causal Parity Thesis by offering principles of causal selection that are spelled out in terms of an explicit philosophical account of causation, namely an interventionist account. I show that two such accounts that have been developed, although they contain important insights about causation in biology, nonetheless fail to provide an adequate reply to the Causal Parity challenge: Ken Waters's account of actual-difference making and Jim Woodward's account of causal specificity. A combination of the two also doesn't do the trick, nor does Laura Franklin-Hall's account of explanation (in this volume). We need additional conceptual resources. I argue that the resources we need consist in a special class of counterfactual conditionals, namely counterfactuals the antecedents of which describe biologically normal interventions. (shrink)

Causal models provide a framework for precisely representing complex causal structures, where specific models can be used to efficiently predict, infer, and explain the world. At the same time, we often do not know the full causal structure a priori and so must learn it from data using a causal model search algorithm. This chapter provides a general overview of causal models and their uses, with a particular focus on causal graphical models (the most (...) commonly used causal modeling framework) and methods for learning such models from observational and experimental data. The chapter concludes with two examples of productive causal search and modeling in experimental philosophy. (shrink)

This paper provides a general introduction to the use of causal models in the metaphysics of causation, specifically structural equation models and directed acyclic graphs. It reviews the formal framework, lays out a method of interpretation capable of representing different underlying metaphysical relations, and describes the use of these models in analyzing causation.

The recent literature on causality has seen the introduction of several distinctions within causality, which are thought to be important for understanding the widespread scientific practice of focusing causal explanations on a subset of the factors that are causally relevant for a phenomenon. Concepts used to draw such distinctions include, among others, stability, specificity, proportionality, or actual-difference making. In this contribution, I propose a new distinction that picks out an explanatorily salient class of causes in biological systems. Some (...) select causes in complex biological systems, I argue, have the property of enabling coherent causal control of these systems. Examples of such control variables include hormones and other signaling molecules, e.g., TOR (target of rapamycin), morphogens or the products of homeotic selector genes in embryonic pattern formation. I propose an analysis of this notion based on concepts borrowed from causal graph theory. (shrink)

The epidemiologist Bradford Hill famously argued that in epidemiology, specificity of association (roughly, the fact that an environmental or behavioral risk factor is associated with just one or at most a few medical outcomes) is strong evidence of causation. Prominent epidemiologists have dismissed Hill’s claim on the ground that it relies on a dubious `one-cause one effect’ model of disease causation. The paper examines this methodological controversy, and argues that specificity considerations do have a useful role to play (...) in causal inference in epidemiology. More precisely, I argue that specificity considerations help solve a pervasive inferential problem in contemporary epidemiology: the problem of determining whether an exposure-outcome correlation might be due to confounding by a social factor. This examination of specificity has interesting consequences for our understanding of the methodology of epidemiology. It highlights how the methodology of epidemiology relies on local tools designed to address specific inference problems peculiar to the discipline, and shows that observational causal inference in epidemiology can proceed with little prior knowledge of the causal structure of the phenomenon investigated. I also argue that specificity of association cannot (despite claims to the contrary) be entirely explained in terms of Woodward’s well-known concept of “one-to-one” causal specificity. This is because specificity as understood by epidemiologists depends on whether an exposure (or outcome) is associated with a `heterogeneous’ set of variables. This dimension of heterogeneity is not captured in Woodward’s notion, but is crucial for understanding the evidential import of specificity of association. (shrink)

This paper derives from a broader project dealing with the notion of causal order. I use this term to signify two kinds of parts-whole dependence: Orderly systems have rich, decomposable, internal structure; specifically, parts play differential roles, and interactions are primarily local. Disorderly systems, in contrast, have a homogeneous internal structure, such that differences among parts and organizational features are less important. Orderliness, I suggest, marks one key difference between individuals and collectives. My focus here will be the connection (...) between order and robustness, i.e. functional resilience in the face of internal or environmental perturbations. I distinguish three varieties of robustness. Ordered robustness is grounded in the system’s specific organizational pattern. In contrast, disorderly robustness stems from the aggregate outcome of many similar parts. In between, we find semi-ordered robustness, wherein a messy ensemble of elements is subjected to a selection or stabilization mechanism. I give brief characterizations of each category, discuss examples and remark on the connection between the order/disorder axis and the notions of individual versus collective. (shrink)

Hitchcock demonstrated that the validity of causal exclusion arguments as well as the plausibility of several of their premises hinges on the specific theory of causation endorsed. In this paper I show that the validity of causal exclusion arguments—if represented within the theory of causal Bayes nets the way Gebharter suggests—actually requires much weaker premises than the ones which are typically assumed. In particular, neither completeness of the physical domain nor the no overdetermination assumption are required.

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)

The problem of structure and agency has been the subject of intense debate in the social sciences for over 100 years. This book offers a solution. Using a critical realist version of the theory of emergence, Dave Elder-Vass argues that, instead of ascribing causal significance to an abstract notion of social structure or a monolithic concept of society, we must recognise that it is specific groups of people that have social structural power. Some of these groups are entities with (...) emergent causal powers, distinct from those of human individuals. Yet these powers also depend on the contributions of human individuals, and this book examines the mechanisms through which interactions between human individuals generate the causal powers of some types of social structures. The Causal Power of Social Structures makes particularly important contributions to the theory of human agency and to our understanding of normative institutions. (shrink)

The statistical community has brought logical rigor and mathematical precision to the problem of using data to make inferences about a model’s parameter values. The TETRAD project, and related work in computer science and statistics, aims to apply those standards to the problem of using data and background knowledge to make inferences about a model’s specification. We begin by drawing the analogy between parameter estimation and model specification search. We then describe how the specification of a structural equation model entails (...) familiar constraints on the covariance matrix for all admissible values of its parameters; we survey results on the equivalence of structural equation models, and we discuss search strategies for model specification. We end by presenting several algorithms that are implemented in the TETRAD II program. (shrink)

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A (...) causes B and B causes C, that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory led to transitive causal judgments. On the other hand, chains schematized as multiple chunks led to intransitive judgments despite strong intermediate links. Normative accounts of causal intransitivity could not explain these intransitive judgments. (shrink)

The principle of 'information causality' can be used to derive an upper bound---known as the 'Tsirelson bound'---on the strength of quantum mechanical correlations, and has been conjectured to be a foundational principle of nature. In this paper, however, I argue that the principle has not to date been sufficiently motivated to play this role; the motivations that have so far been given are either unsatisfactorily vague or else amount to little more than an appeal to intuition. I then consider how (...) one might begin to successfully motivate the principle. I argue that a compelling way of so doing is to understand it as a generalisation of Einstein's principle of the mutually independent existence---the 'being-thus'---of spatially distant things, interpreted as a special methodological principle. More specifically: I describe an argument, due to Demopoulos, to the effect that the quantum-mechanical no-signalling condition can be viewed as a generalisation, appropriate to an irreducibly statistical theory such as quantum mechanics, of the Einsteinian principle. And I then argue that a compelling way to motivate information causality is to in turn consider it as a further generalisation of the Einsteinian principle that is appropriate to a theory of communication. I nevertheless describe important obstacles that must yet be overcome if the project of establishing information causality as a foundational principle of nature is to succeed. (shrink)

This article examines how specific realist and projectivist versions of manipulability theories of causation deal with the problem of objectivity. Does an agent-dependent concept of manipulability imply that conflicting causal claims made by agents with different capacities can come out as true? In defence of the projectivist stance taken by the agency view, I argue that if the agent’s perspective is shown to be uniform across different agents, then the truth-values of causal claims do not vary arbitrarily and, (...) thus, reach a satisfactory level of objectivity. My argument connects Price’s considerations on the situation of deliberation, whose structure, common to all agents, is the same with respect to both decision making and causal claims on a concept inspired by Douglas’s classification of objectivity of thought processes: the perspective of the detached agent. I further argue that, despite his agent-independent concept of intervention, Woodward’s claim of a stronger objectivity standard cannot be achieved, as the relativity of causal concepts to a variable set brings about the issue of the agent’s choice of variables. Consequently, a more permissive objectivity standard applies to both views. (shrink)

While scientific realism generally assumes that successful scientific explanations yield information about reality, realists also have to admit that not all information acquired in this way is equally well warranted. Some versions of scientific realism do this by saying that explanatory posits with which we have established some kind of causal contact are better warranted than those that merely appear in theoretical hypotheses. I first explicate this distinction by considering some general criteria that permit us to distinguish causal (...) warrant from theoretical warrant. I then apply these criteria to a specific case from particle physics, claiming that scientific realism has to incorporate the distinction between causal and theoretical warrant if it is to be an adequate stance in the philosophy of particle physics. (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)

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)

Moritz Schlick’s interpretation of the causality principle is based on Schlick’s understanding of quantum mechanics and on his conviction that quantum mechanics strongly supports an empiricist reading of causation in his sense. The present paper compares the empiricist position held by Schlick with Bas van Fraassen’s more recent conception of constructive empiricism. It is pointed out that the development from Schlick’s understanding of logical empiricism to constructive empiricism reflects a difference between the understanding of quantum mechanics endorsed by Schlick and (...) the understanding that had been established at the time of van Fraassen’s writing. (shrink)

How do cognitive neuroscientists explain phenomena like memory or language processing? This book examines the different kinds of experiments and manipulative research strategies involved in understanding and eventually explaining such phenomena. Against this background, it evaluates contemporary accounts of scientific explanation, specifically the mechanistic and interventionist accounts, and finds them to be crucially incomplete. Besides, mechanisms and interventions cannot actually be combined in the way usually done in the literature. This book offers solutions to both these problems based on insights (...) from experimental practice. It defends a new reading of the interventionist account, highlights the importance of non-interventionist studies for scientific inquiry, and supplies a taxonomy of experiments that makes it easy to see how the gaps in contemporary accounts of scientific explanation can be filled. The book concludes that a truly empirically adequate philosophy of science must take into account a much wider range of experimental research than has been done to date. With the taxonomy provided, this book serves a stepping-stone leading into a new era of philosophy of science—for cognitive neuroscience and beyond. (shrink)

The causal problem has become topical once again. While we are no longer causalists or believers in the universal truth of the causal principle we continue to think of causes and effects, as well as of causal and noncausal relations among them. Instead of becoming indeterminists we have enlarged determinism to include noncausal categories. And we are still in the process of characterizing our basic concepts and principles concerning causes and effects with the help of exact tools. (...) This is because we want to explain, not just describe, the ways of things. The causal principle is not the only means of understanding the world but it is one of them. The demand for a fourth edition of this distinguished book on the subject of causality is clear evidence that this principle continues to be an important and popular area of philosophic enquiry. Non-technical and clearly written, this book focuses on the ontological problem of causality, with specific emphasis on the place of the causal principle in modern science. Mario Bunge first defines the terminology employed and describes various formulations of the causal principle. He then examines the two primary critiques of causality, the empiricist and the romantic, as a prelude to the detailed explanation of the actual assertions of causal determinism. Bunge analyzes the function of the causal principle in science, touching on such subjects as scientific law, scientific explanation, and scientific prediction. In so doing, he offers an education to layman and specialist alike on the history of a concept and its opponents. Professor William A. Wallace, author of Causality and Scientific Explanation said of an earlier edition of this work: "I regard it as a truly seminal work in this field.". (shrink)

Powers, capacities and dispositions (in what follows I will use these terms synonymously) have become prominent in recent debates in metaphysics, philosophy of science and other areas of philosophy. In this paper I will analyse in some detail a well-known argument from scientific practice to the existence of powers/capacities/dispositions. According to this argument the practice of extrapolating scientific knowledge from one kind of situation to a different kind of situation requires a specific interpretation of laws of nature, namely as attributing (...) dispositions to systems. My main interest will be to discuss what characteristics these dispositions need to have in order to account for the scientific practice in question. I will furthermore assess whether the introduction of dispositions in the context of the extrapolation argument can be described as a ‘revitalization’ or as a ‘return’ to those notions repudiated by early modern philosophers. More particularly I will argue for the following claims: I. In repudiating scholastic terminology, including substantial forms with their causal powers, post-cartesian philosophers focussed on a concept of causation that was much stronger than 21st century conceptions of causation. For this reason alone, whatever ‘causal’ is supposed to mean in today’s causal powers, embracing causal powers is not a simple return to a pre-cartesian notion. II. The dispositions presupposed in scientific practice need not (and should not) be construed in causal terms (whether strong or weak). III. While some early modern philosophers contrasted the characterisation of the natural world in terms of substantial forms (and their causal powers) on the one hand and a mathematical characterization on the other and suggested that these approaches are incompatible, the dispositions postulated by the extrapolation argument to account for scientific practice are themselves characterized in mathematical terms. More precisely: The behaviour the systems are disposed to display is – at least in physics – often characterized in mathematical terms. IV. The dispositions assumed in the law-statements in scientific practice are determinable rather than determinate properties. (shrink)

Causal compatibilism claims that even though physics is causally closed, and even though mental properties are multiply realizable and are not identical to physical causal properties, mental properties are causal properties nonetheless. This position asserts that there is genuine causation at multiple descriptive/ontological levels; physics-level causal claims are not really incompatible with mentalistic causal claims. I articulate and defend a version of causal compatibilism that incorporates three key contentions. First, causation crucially involves robust patterns (...) of counterfactual dependence among properties.Second, often several distinct such patterns, all subsuming a single phenomenon, exist at different descriptive/ontological levels (e.g., microphysical, neurobiological, macrobiological, and psychological). Third, the concept of causation is governed by an implicit contextual parameter that normally determines a specific descriptive/ontological level as the contextually relevant level, for the context-sensitive semantic evaluation of causal statements. (shrink)

This paper suggests and discusses an answer to the following question: What distinguishes causal from non-causal or coincidental co-occurrences? The answer derives from Elizabeth Anscombe’s idea that causality is a highly abstract concept whose meaning derives from our understanding of specific causally productive activities, and from her rejection of the assumption that causality can be informatively understood in terms of actual or counterfactual regularities.Keywords: Elizabeth Anscombe; Causality; Explanation; Inhibition.

Peter Alexander: It is presumably admitted that laws, whether causal or not, are universal in form; they are appropriately stated in universal categoricals or unrestricted hypotheticals. I assume that this is not at issue in the question set. I take our question to be this: given that causal laws are universal statements, can they be said to be about, to apply to, to hold for, individual things? -/- Peter Downing: Mr. Alexander maintains that there are 'irreducibly singular' (...) class='Hi'>causal statements, and that from this it follows that causal laws need not be purely general. The view that there are irreducibly singular causal statements appears to commit Alexander to rejecting two theses: (i) that singular causal statements are analysable into statements containing specific generalizations, [...] (ii) That singular causal statements are analysable into statements containing incompletely specified generalizations [...]. Thesis (i) is, I think, clearly false. [...] Thesis (ii), however, is not easily refuted. (shrink)

Ernst Mayr proposed a distinction between “proximate”, mechanistic, and “ultimate”, evolutionary, causes of biological phenomena. This dichotomy has influenced the thinking of many biologists, but it is increasingly perceived as impeding modern studies of evolutionary processes, including study of “niche construction” in which organisms alter their environments in ways supportive of their evolutionary success. Some still find value for this dichotomy in its separation of answers to “how?” versus “why?”questions about evolution. But “why is A?” questions about evolution necessarily take (...) the form “how does A occur?”, so this separation is illusory. Moreover, the dichotomy distorts our view of evolutionary causality, in that, contra Mayr, the action of natural selection, driven by genotype-phenotype-environment interactions which constitute adaptations, is no less “proximate” than the biological mechanisms which are altered by naturally selected genetic variants. Mayr’s dichotomy thus needs replacement by more realistic, mechanistic views of evolution. From a mechanistic viewpoint, there is a continuum of adaptations from those evolving as responses to unchanging environmental pressures to those evolving as the capacity for niche construction, and intermediate stages of this can be identified. Some biologists postulate an association of “phenotypic plasticity” (phenotype-environment covariation with genotype held constant) with capacity for niche construction. Both “plasticity” and niche construction comprise wide ranges of adaptive mechanisms, often fully heritable and resulting from case-specific evolution. Association of “plasticity” with niche construction is most likely to arise in systems wherein capacity for complex learning and behavioral flexibility have already evolved. (shrink)