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.

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)

Reductionists in biology claim that all biological events can be explained in terms of genes and macromolecules alone, while antireductionists argue that some biological events must be explained at a higher level. The literature, however, does not distinguish between different kinds of molecular explanation. The goal of this article is to identify and analyze three such kinds. The analysis of molecular explanations herein carries an important philosophical implication; in shunning crude reductionism and extreme versions of holism, we can combine the (...) insights of thoughtful reductionists with sophisticated antireductionism. When this is done, the question of explanatory reductionism becomes less substantial than often supposed. (shrink)

This essay is concerned with concentrations of entities, which play an important—albeit often overlooked—role in scientific explanation. First, I discuss an example from molecular biology to show that concentrations can play an irreducible causal role. Second, I provide a preliminary philosophical analysis of this causal role, suggesting some implications for extant theories of causation. I conclude by introducing the concept of causation by concentration, a form of statistical causation whose widespread presence throughout the sciences has been unduly neglected and which (...) deserves to be studied in more depth. 1 Introduction2 Solving Lillie's Paradox: Lysogenic Induction in Phage λ3 Repressor Concentration and the Tuning of the Switch4 Concentration and Causality5 Preemption in Concentrations: Analysis and Implications6 Causation by Concentration: General Definition, Refinements, and Further Applications. (shrink)

If someone brings about an outcome without intending to, is she causally and morally responsible for it? What if she acts intentionally, but as the result of manipulation by another agent? Previous research has shown that an agent's mental states can affect attributions of causal and moral responsibility to that agent, but little is known about what effect one agent's mental states can have on attributions to another agent. In Experiment 1, we replicate findings that manipulation lowers attributions of responsibility (...) to manipulated agents. Experiments 2–7 isolate which features of manipulation drive this effect, a crucial issue for both philosophical debates about free will and attributions of responsibility in situations involving social influence more generally. Our results suggest that “bypassing” a manipulated agent's mental states generates the greatest reduction in responsibility, and we explain our results in terms of the effects that one agent's mental states can have on the counterfactual relations between another agent and an outcome. (shrink)

Mechanistic explanations are often said to explain because they reveal the causal structure of the world. Conversely, dynamical models supposedly lack explanatory power because they do not describe causal structure. The only way for dynamical models to produce causal explanations is via the 3M criterion: the model must be mapped onto a mechanism. This framing of the situation has become the received view around the viability of dynamical explanation. In this paper, I argue against this position and show that dynamical (...) models can themselves reveal causal structure and consequently produce nonmechanistic, dynamical explanations. Taking the example of cell fates from systems biology, I show how dynamical models, and specifically the attractor landscapes they describe, identify the causes of cell differentiation and explain why cells select particular fates. These dynamical features of the system better fit Woodward’s (2010, 2018) criteria of specificity and proportionality and make them the best candidate causes of cell fates than mechanisms. I also show how these causes are irreducible and inaccessible to mechanistic models, making 3M unworkable and counterproductive in this case. Dynamical models can reveal dynamical causes and thereby provide causal explanations. (shrink)

The “negative view” is the claim that natural selection cannot explain why a particular individual has one trait, rather than another. Here, I modify an example from Lewens (2001) to show that this claim is sometimes false. I then advance a variation on the negative view. It is the claim that selection at the organism level within a lineage cannot explain why a particular individual in that lineage has one allele, rather than another. This formulation better describes the explanatory role (...) of selection. (shrink)

The “negative view” is the claim that natural selection cannot explain why a particular individual has one trait, rather than another. Here, I modify an example from Lewens to show that this claim is sometimes false. I then advance a variation on the negative view. It is the claim that selection at the organism level within a lineage cannot explain why a particular individual in that lineage has one allele, rather than another. This formulation better describes the explanatory role of (...) selection. (shrink)

Causal exclusion arguments are taken to threaten the autonomy of the special sciences, and the causal efficacy of mental properties. A recent line of response to these arguments has appealed to “independently plausible” and “well grounded” theories of causation to rebut key premises. In this paper I consider two papers which proceed in this vein and show that they share a common feature: they both require causes to be proportional to their effects. I argue that this feature is a bug, (...) and one that generalises: any attempt to rescue the autonomy of the special sciences, or the efficacy of the mental, from exclusion worries had better not look to proportionality for help. (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)

Behavior genetics is a controversial science. For decades, scholars have sought to understand the role of heredity in human behavior and life-course outcomes. Recently, technological advances and the rapid expansion of genomic databases have facilitated the discovery of genes associated with human phenotypes such as educational attainment and substance use disorders. To maximize the potential of this flourishing science, and to minimize potential harms, careful analysis of what it would mean for genes to be causes of human behavior is needed. (...) In this paper, we advance a framework for identifying instances of genetic causes, interpreting those causal relationships, and applying them to advance causal knowledge more generally in the social sciences. Central to thinking about genes as causes is counterfactual reasoning, the cornerstone of causal thinking in statistics, medicine, and philosophy. We argue that within-family genetic effects represent the product of a counterfactual comparison in the same way as average treatment effects (ATEs) from randomized controlled trials (RCTs). Both ATEs from RCTs and within-family genetic effects are shallow causes: They operate within intricate causal systems (non-unitary), produce heterogeneous effects across individuals (non-uniform), and are not mechanistically informative (non-explanatory). Despite these limitations, shallow causal knowledge can be used to improve understanding of the etiology of human behavior and to explore sources of heterogeneity and fade-out in treatment effects. (shrink)

Much microbiota-gut-brain research focuses on the causal role of microbiomes as a whole, rather than their component parts: microbes. Hooks et al. find these whole-community explanations inadequate; however, they do not provide suggestions for better explanations. By appealing to proportionality – a criterion that can be used to develop more appropriate causal explanations – more accurate causal claims can be made.

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)

This paper aims to clarify the consequences of new scientific and philosophical approaches for the practical-theoretical framework of modern developmental biology. I highlight normal development, and the instructive-permissive distinction, as key parts of this framework which shape how variation is conceptualised and managed. Furthermore, I establish the different dimensions of biological variation: the units, temporality and mode of variation. Using the analytical frame established by this, I interpret a selection of examples as challenges to the instructive-permissive distinction. These examples include (...) the phenomena of developmental plasticity and transdifferentiation, the role of the microbiome in development, and new methodological approaches to standardisation and the assessment of causes. Furthermore, I argue that investigations into organismal development should investigate the effects of a wider range of kinds of variation including variation in the units, modes and temporalities of development. I close by examining various possible opportunities for producing and using normal development free of the assumptions of the instructive-permissive distinction. These opportunities are afforded by recent developments, which include new ways of producing standards incorporating more natural variation and being based on function rather than structure, and the ability to produce, store, and process large quantities of data. (shrink)

Lawton’s contingency thesis states that there are no useful generalizations at the level of ecological communities because these systems are especially prone to contingent historical events. I argue that this influential thesis has been grounded on the wrong kind of evidence. CT is best understood in Woodward’s terms as a claim about the instability of certain causal dependencies across different background conditions. A recent distinction between evolution and ecology reveals what an adequate test of Lawton’s thesis would look like. To (...) date, CT remains untested. But developments in genome and molecular ecology point in a promising direction. (shrink)

Scientific models are often said to be more or less general depending on how many cases they cover. In this paper we argue that the cardinality of cases is insufficient as a metric of generality, and we present a novel account based on measure theory. This account overcomes several problems with the cardinality approach, and additionally provides some insight into the nature of assessments of generality. Specifically, measure theory affords a natural and quantitative way of describing local spaces of possibility. (...) The generality of models can be understood as the measure of possibilities to which the models apply. In order to illustrate our view, we consider the example of structural genomics, the ongoing project of building three-dimensional models of biological macromolecules like proteins. Using measure theory, we interpret the practice of homology modeling, where such models are understood to apply widely but imperfectly to the space of possible proteins. (shrink)

The aim of this article is to critically build on Justin Biddle and Anna Leuschner’s characterization of epistemologically detrimental dissent in the context of science. We argue that the presence of nonepistemic agendas and severe nonepistemic consequences offers neither necessary nor sufficient conditions for EDD to obtain. We clarify their role by arguing that they are contingent enabling factors, not stable difference-makers, in the production of EDD. We maintain that two stable difference-makers are core to the production of EDD: production (...) of skewed science and effective public dissemination. (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)

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)

Philosophical discussions on causal inference in medicine are stuck in dyadic camps, each defending one kind of evidence or method rather than another as best support for causal hypotheses. Whereas Evidence Based Medicine advocates the use of Randomised Controlled Trials and systematic reviews of RCTs as gold standard, philosophers of science emphasise the importance of mechanisms and their distinctive informational contribution to causal inference and assessment. Some have suggested the adoption of a pluralistic approach to causal inference, and an inductive (...) rather than hypothetico-deductive inferential paradigm. However, these proposals deliver no clear guidelines about how such plurality of evidence sources should jointly justify hypotheses of causal associations. We here develop such guidelines by first giving a philosophical analysis of the underpinnings of Hill’s viewpoints on causality. We then put forward an evidence-amalgamation framework adopting a Bayesian net approach to model causal inference in pharmacology for the assessment of harms. Our framework accommodates a number of intuitions already expressed in the literature concerning the EBM vs. pluralist debate on causal inference, evidence hierarchies, causal holism, relevance, and reliability. (shrink)

Many authors have turned their attention to the notion of constitution to determine whether the hypothesis of extended cognition (EC) is true. One common strategy is to make sense of constitution in terms of the new mechanists’ mutual manipulability account (MM). In this paper I will show that MM is insufficient. The Challenge of Trivial Extendedness arises due to the fact that mechanisms for cognitive behaviors are extended in a way that should not count as verifying EC. This challenge can (...) be met by adding a necessary condition: cognitive constituents satisfy MM and they are what I call behavior unspecific. (shrink)

Recent mechanistic philosophers :1287–1321, 2016) have argued that the cognitive sciences are not autonomous from neuroscience proper. I clarify two senses of autonomy–metaphysical and epistemic—and argue that cognitive science is still autonomous in both senses. Moreover, mechanistic explanation of cognitive phenomena is not therefore an alternative to the view that cognitive science is autonomous of neuroscience. If anything, it’s a way of characterizing just how cognitive processes are implemented by neural mechanisms.

Distinguishing mechanistic components from mere causally relevant background conditions remains a difficulty for mechanistic accounts of explanation. By distinguishing resources from mechanical parts, I argue that we can more effectively draw this boundary. Further, the distinction makes obvious that there are distinctive resource explanations which are not captured by a traditional part-based mechanistic account. While this suggests a straightforward extension of the mechanistic model, I argue that incorporating resources and resource explanations requires moving beyond the purely local account of levels (...) that some mechanists advocate. (shrink)

At present, the science of consciousness is structured around the search for the neural correlates of consciousness. One of the alleged advantages of the NCCs framework is its metaphysical neutrality—the fact that it begs no contested questions with respect to debates about the fundamental nature of consciousness. Here, we argue that even if the NCC framework is metaphysically neutral, it is structurally committed, for it presupposes a certain model—what we call the Lite-Brite model—of consciousness. This, we argue, represents a serious (...) liability for the NCC framework for the plausibility of the Lite-Brite model is very much an open question, and the science of consciousness would be better served by a framework that does not presuppose it. Drawing on interventionist ideas in the philosophy of science, we suggest that the Difference-Maker framework can provide just such an alternative. Instead of searching for the neural correlates of consciousness, we ought to be searching for the difference makers of consciousness. We detail how a shift to searching DMCs will change both the practice of consciousness science and the interpretation of existing results. (shrink)

Contrastive neuroimaging is often taken to provide evidence about the localization of cognitive functions. After canvassing some problems with this approach, I offer an alternative: neuroimaging gives evidence about regions of the brain that bear difference-making relationships to psychological processes of interest. I distinguish between the specificity and what I call the systematicity of a difference-making relationship, and I show how at least some neuroimaging experiments can give evidence for systematic difference-making.

L. A. Paul and Ned Hall’s book makes an original and important contribution to the philosophical debate on causation. Their aim is not to construct a theory of causation but “to sketch a map” of the “landscape” (1) constituted by a rich set of problem cases and various theories of causation devised to account for them.Chapter 1 presents the scope and aim of the book, justifies the method of evaluating theories of causation by exploring whether they are refuted by counterexamples, (...) and provides an overview of the rest of the book.Chapter 2 justifies the choice of problems and solutions considered in the book. Several assumptions are made from the beginning. Causation is taken to be a relation among particular events rather than types of events. A “reductionist outlook” is adopted, according to which facts about what causes what are determined by (1) facts about what happens and (2) fundamental laws. These assumptions provide the background for a “minimal sufficiency account” of causation .. (shrink)

This article considers the popular thesis that a more proportional relationship between a cause and its effect yields a more abstract causal explanation of that effect, which in turn produces a deeper explanation. This thesis is taken to have important implications for choosing the optimal granularity of explanation for a given explanandum. In this article, I argue that this thesis is not generally true of probabilistic causal relationships. In light of this finding, I propose a pragmatic, interest-relative measure of explanatory (...) depth. This measure uses a decision-theoretic model of information pricing to determine the optimal granularity of explanation for a given explanandum, agent, and decision problem. (shrink)

Proponents of various causal exclusion arguments claim that for any given event, there is often a unique level of granularity at which that event is caused. Against these causal exclusion arguments, causal ecumenists argue that the same event or phenomenon can be caused at multiple levels of granularity. This paper argues that the Bayesian network approach to representing the causal structure of target systems is consistent with causal ecumenism. Given the ubiquity of Bayesian networks as a tool for representing causal (...) structure in both philosophy of science and science itself, this result speaks in favor of the ecumenical view, and against rival exclusionary accounts. Gebharter’s (Philos Phenomenol Res 95(2):353–375, 2017) argument that the Bayes nets formalism is consistent with causal exclusion is considered and rebutted. (shrink)

In experimental settings, scientists often “make” new things, in which case the aim is to intervene in order to produce experimental objects and processes—characterized as ‘effects’. In this discussion, I illuminate an important performative function in measurement and experimentation in general: intervention-based experimental production (IEP). I argue that even though the goal of IEP is the production of new effects, it can be informative for causal details in scientific representations. Specifically, IEP can be informative about causal relations in: regularities under (...) study; ‘intervention systems’, which are measurement/experimental systems; and new technological systems. (shrink)

In experimental settings, scientists often “make” new things, in which case the aim is to intervene in order to produce experimental objects and processes—characterized as ‘effects’. In this discussion, I illuminate an important performative function in measurement and experimentation in general: intervention-based experimental production. I argue that even though the goal of IEP is the production of new effects, it can be informative for causal details in scientific representations. Specifically, IEP can be informative about causal relations in: regularities under study; (...) ‘intervention systems’, which are measurement/experimental systems; and new technological systems. (shrink)

Racial disparities in health outcomes have recently become a flashpoint in the debate about the value of race as a biological concept. What role, if any, race has in the etiology of disease is a philosophically and scientifically contested topic. In this article, I expand on the insights of the new mechanistic philosophy of science to defend a mechanism discovery approach to investigating epidemiological racial disparities. The mechanism discovery approach has explanatory virtues lacking in the populational approach typically employed in (...) the study of race and biomedicine. The explanatory constraints that form an integral part of the new mechanistic approach enable mechanism discovery to avoid the epistemic and normative shortcomings of the populational approach. The methodology of mechanism discovery can fruitfully be extended to the treatment and reversal of epidemiological racial disparities. (shrink)

It's not uncommon for scientists to give different explanations of the same phenomenon, but we currently lack clear guidelines for deciding whether to treat such accounts as competitors. This article discusses how science studies can help create tools and guidelines for thinking about whether explanations compete. It also specifies how one family of discourse rules enables there to be differing accounts that appear to compete but don't. One hopes that being more aware of the linguistic mechanisms making compatible accounts appear (...) to compete will prevent people from wasting resources trying to show which account is right. (shrink)

This paper sets out to analyze how causation works by focusing on biology, as represented by epidemiology and by scientific information on how the body works (“physiology”). It starts by exploring the specificity of evolved physiological systems, in which evolutionary, developmental and proximal causes all fit together, and the concept of function is meaningful; in contrast, this structure does not apply in epidemiology (or outside biology). Using these two contrasting branches of biology, I examine the role both of mechanism and (...) of difference making in causation. I find that causation necessarily involves both mechanism and difference making, and that they play complementary roles. Both are seen as ontologically necessary, even if the evidence is not always available for both. Influential monist accounts that focus on one of these, at the expense of ignoring the other, are found to be inadequate on these and on other grounds. Recent attempts to combine them are reviewed, notably that of Russo and Williamson (Int Stud Philos Sci 21:157–170, 2007), and it is argued that their epistemic view requires there to be a source of the different types of evidence that a rational agent would consider, and that this source must be ontic. I then analyze how causal relationships work in evolved physiological systems and in those studied by epidemiology, with a particular focus on how mechanism interacts with input. Finally, I consider this concept of causation from the perspective of everyday language, and of its possible generalisability outside biology. (shrink)

Several authors have recently claimed that the notorious causal exclusion problem, according to which higher-level causes are threatened with causal pre-emption by lower-level causes, can be avoided if causal relevance is understood in terms of Woodward's interventionist account of causation. They argue that if causal relevance is defined in interventionist terms, there are cases where only higher-level properties, but not the lower-level properties underlying them, qualify as causes of a certain effect. In this article, I show that the line of (...) reasoning supposed to establish this claim does not succeed and that interventionism is not better capable of dealing with higher-level causal claims than other accounts of causation. According to Woodward, higher-level causal claims are nonetheless more adequate than lower-level ones if they describe a realization-independent dependency relationship and, hence, meet the requirement that causes should be proportional to their effects. I argue, however, that combining interventionism with proportionality considerations raises difficulties and that, therefore, Woodward's account does not vindicate higher-level causation. (shrink)

When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are smoothly performed without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist: that is, they cast skillful performance as a knowledge-driven process. Optimal motor control theory, as representative par excellence of such approaches, casts skillful performance as an instruction, instantiated in the brain, that needs to be executed—a motor command. This paper aims (...) to show the limitations of such instructionist approaches to skillful performance. We specifically address the question of whether the assumption of control-theoretic models is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists of the execution of theoretical instructions harnessed in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from OMCT. The final sections of this paper examine predictive coding and active inference—behavioral modeling frameworks that descend, but are distinct, from OMCT—and argue that the instructionist, control-theoretic assumptions are ill-motivated in light of new developments in active inference. (shrink)

It is often thought that non-junk or coding DNA is more significant than other cellular elements, including so-called junk DNA. This is for two main reasons: because coding DNA is often targeted by historical or current selection, it is considered functionally special and because its mode of action is uniquely specific amongst the other actual difference makers in the cell, it is considered causally special. Here, we challenge both these presumptions. With respect to function, we argue that there is previously (...) unappreciated reason to think that junk DNA is significant, since it can alter the cellular environment, and those alterations can influence how organism-level selection operates. With respect to causality, we argue that there is again reason to think that junk DNA is significant, since it too is remarkably causally specific. As a result, something is missing from the received view of significance in molecular biology—a view which emphasizes specificity and neglects something we term ‘reach’. With the special case of junk DNA in mind, we explore how to model and understand the causal specificity, reach, and corresponding efficacy of difference makers in biology. The account contains implications for how evolution shapes the genome, as well as advances our understanding of multi-level selection. (shrink)

In this paper, I argue that looking at the concept of neural function through the lens of cognition alone risks cognitive myopia: it leads neuroscientists to focus only on mechanisms with cognitive functions that process behaviorally relevant information when conceptualizing “neural function”. Cognitive myopia tempts researchers to neglect neural mechanisms with noncognitive functions which do not process behaviorally relevant information but maintain and repair neural and other systems of the body. Cognitive myopia similarly affects philosophy of neuroscience because scholars overlook (...) noncognitive functions when analyzing issues surrounding e.g., functional decomposition or the multifunctionality of neural structures. I argue that we can overcome cognitive myopia by adopting a patchwork approach that articulates cognitive and noncognitive “patches” of the concept of neural function. Cognitive patches describe mechanisms with causally specific effects on cognition and behavior which are likely operative in transforming sensory or other inputs into motor outputs. Noncognitive patches describe mechanisms that lack such specific effects; these mechanisms are enabling conditions for cognitive functions to occur. I use these distinctions to characterize two noncognitive functions at the mesoscale of neural circuits: subsistence functions like breathing are implemented by central pattern generators and are necessary to maintain the life of the organism. Infrastructural functions like gain control are implemented by canonical microcircuits and prevent neural system damage while cognitive processing occurs. By adding conceptual patches that describe these functions, a patchwork approach can overcome cognitive myopia and help us explain how the brain’s capacities as an information processing device are constrained by its ability to maintain and repair itself as a physiological apparatus. (shrink)

Counterfactual conditionals have been appealed to in various ways to show how the mind can be causally efficacious. However, it has often been overestimated what the truth of certain counterfactuals actually indicates about causation. The paper first identifies four approaches that seem to commit precisely this mistake. The arguments discussed involve erroneous assumptions about the connection of counterfactual dependence and genuine causation, as well as a disregard of the requisite evaluation conditions of counterfactuals. In a second step, the paper uses (...) the insights of the foregoing analyses to formulate a set of counterfactuals-based conditions that are characterized as sufficient to establish singular causal claims. The paper concludes that there are ample reasons to believe that some mental events satisfy all these conditions with respect to certain further events and, hence, that mental events sometimes are causes. (shrink)

In cases in which many causes together bring about an effect, it is common to select some as particularly important. Philosophers since Mill have been pessimistic about analyzing this reasoning because of its variability and the multifarious causal and pragmatic details of how it works. I argue Mill was right to think these details matter but wrong that they preclude philosophical analysis of causal selection. I show that analyzing the pragmatic details of scientific debates about the important causes of the (...) Bhopal Gas Tragedy can illuminate causal reasoning about disasters and shed new light on causality and causal selection. (shrink)

Systems biologists often distance themselves from reductionist approaches and formulate their aim as understanding living systems “as a whole.” Yet, it is often unclear what kind of reductionism they have in mind, and in what sense their methodologies would offer a superior approach. To address these questions, we distinguish between two types of reductionism which we call “modular reductionism” and “bottom-up reductionism.” Much knowledge in molecular biology has been gained by decomposing living systems into functional modules or through detailed studies (...) of molecular processes. We ask whether systems biology provides novel ways to recompose these findings in the context of the system as a whole via computational simulations. As an example of computational integration of modules, we analyze the first whole-cell model of the bacterium M. genitalium. Secondly, we examine the attempt to recompose processes across different spatial scales via multi-scale cardiac models. Although these models rely on a number of idealizations and simplifying assumptions as well, we argue that they provide insight into the limitations of reductionist approaches. Whole-cell models can be used to discover properties arising at the interfaces of dynamically coupled processes within a biological system, thereby making more apparent what is lost through decomposition. Similarly, multi-scale modeling highlights the relevance of macroscale parameters and models and challenges the view that living systems can be understood “bottom-up.” Specifically, we point out that system-level properties constrain lower-scale processes. Thus, large-scale modeling reveals how living systems at the same time are more and less than the sum of the parts. (shrink)

We present two case studies from contemporary biology in which we observe conflicts between established and emerging approaches. The first case study discusses the relation between molecular biology and systems biology regarding the explanation of cellular processes, while the second deals with phylogenetic systematics and the challenge posed by recent network approaches to established ideas of evolutionary processes. We show that the emergence of new fields is in both cases driven by the development of high-throughput data generation technologies and the (...) transfer of modeling techniques from other fields. New and emerging views are characterized by different philosophies of nature, i.e. by different ontological and methodological assumptions and epistemic values and virtues. This results in a kind of conflict we call “epistemic competition” that manifests in two ways: On the one hand, opponents engage in mutual critique and defense of their fundamental assumptions. On the other hand, they compete for the acceptance and integration of the knowledge they provide by a broader scientific community. Despite an initial rhetoric of replacement, the views as well as the respective audiences come to be seen as more clearly distinct during the course of the debate. Hence, we observe—contrary to many other accounts of scientific change—that conflict results in the formation of new niches of research, leading to co-existence and perceived complementarity of approaches. Our model thus contributes to the understanding of the pluralization of the scientific landscape. (shrink)

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)

Goldman (1967) proposed that a subject s knows p if and only if p is appropriately causally connected to s’s believing p. He later on abandoned this theory (Goldman, 1976). The main objection to the theory is that the causal connection required by Goldman is compatible with certain problematic forms of luck. In this paper we argue that Goldman’s causal theory of knowledge can overcome the luck problem if causation is understood along interventionist lines. We also show that the modified (...) theory leads to the correct results in contexts involving other prominent forms of epistemic luck and compare it with other accounts on the market. (shrink)

Perceptual constancy has played a significant role in philosophy of perception. It figures in debates about direct realism, color ontology, and the minimal conditions for perceptual representation. Despite this, there is no general consensus about what constancyis. I argue that an adequate account of constancy must distinguish it from three distinct phenomena:meresensory stability through proximal change, perceptualcategorizationof a distal dimension, and stability throughirrelevantproximal change. Standard characterizations of constancy fall short in one or more of these respects. I develop an account (...) of constancy that overcomes these problems. The account has two parts: an analysis of constancy mechanisms, and an analysis of the conditions under which a constancy capacity is exercised. I then employ this account to evaluate whether constancy is a necessary condition for perceptual representation, as some have conjectured. I argue that explanatory practice in perceptual psychology fails to support this view. Rather, it fits better with the weaker principle that representation requires specific tracking of a distal dimension. (shrink)

In our commentary on Lynch et al.’s target paper, we focus on decomposition as a research strategy. We argue that not only the presumptive microbial causes but also their supposed phenotypic effects need to be decomposed relative to each other. Such a dual decomposition strategy ought to improve the way in which causal claims in microbiome research can be made and understood.

In our commentary on Lynch et al.’s target paper, we focus on decomposition as a research strategy. We argue that not only the presumptive microbial causes but also their supposed phenotypic effects need to be decomposed relative to each other. Such a dual decomposition strategy ought to improve the way in which causal claims in microbiome research can be made and understood.