This article offers a reading of Eriugena’s thought that is inspired by Heidegger’s claim according to which being is constituted in a dialectical interplay of revelation and concealment. Beginning with an analysis of how “causality as concealing revelation” works on the level of God’s inner-Trinitarian life, the piece moves on to a consideration of the way in which the human soul reveals itself in successive stages of exteriorization that culminate in the creation of the body, its “image.” The body, however, (...) conceals as much as it reveals true human nature. Moreover, it is shown that for Eriugena all of reality, as theophany, possesses this character of concealing its fundamental truth. These insights lead Eriugena to a recognition of radical human finitude, as genuine wisdom requires an acknowledgement of our fundamental ignorance. (shrink)

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

The paper examines definitions of ‘cause’ in the epidemiological literature. Those definitions all describe causes as factors that make a difference to the distribution of disease or to individual health status. In the philosophical jargon, causes in epidemiology are difference-makers. Two claims are defended. First, it is argued that those definitions underpin an epistemology and a methodology that hinge upon the notion of variation, contra the dominant Humean paradigm according to which we infer causality from regularity. Second, despite the (...) fact that causes be defined in terms of ‘difference-making’, this cannot fixes the causal metaphysics. Causality in epidemiology ought to be interpreted according to the epistemic theory. In this approach relations are deemed causal depending on the evidence and on the available methods. Indeed, evidence to establish causal claims requires difference-making considerations; furthermore, those definitions of cause reflect the ‘variational’ epistemology and methodology of epidemiology. (shrink)

Graphical AbstractMicrobiome research attributes to whole microbiomes a causal role in the occurrence of different health outcomes. I argue, following some distinctions about causal relationships and explanations made within a philosophical account of causation, the “interventionist account,” that such claims need more scrutiny.

ABSTRACT Contrastive and deviant/default accounts of causation are becoming increasingly common. However, discussions of these accounts have neglected important questions, including how the context determines the contrasts, and what shared knowledge is necessary for this to be possible. I address these questions, using organic chemistry as a case study. Focusing on one example—nucleophilic substitution—I show that the kinds of causal claims that can be made about an organic reaction depend on how the reaction is modelled, and argue that (...) paying attention to the various ways that reactions are modelled has important implications for our understanding of causation. _1_ Introduction _2_ General Contrastive Causal Claims in Organic Chemistry _3_ Deviant Causal Claims in Organic Chemistry _4_ Nucleophilic Substitution Reactions _5_ The Causal Modelling Tradition _5.1_ The type/token distinction _6_ Competing Reactions _6.1_ Type- and token-causal claims, variables, and values of variables _7_ Disambiguation of ‘Reaction’ _8_ Reaction Kinds _9_ Specific Reactions _9.1_ Specific reactions and token-causal claims _9.2_ Specific reactions and type-causal claims _10_ Implications _10.1_ Kinds of causal claim _10.2_ Contrastive and deviant causal claims _10.3_ Model relativity. (shrink)

Indicative conditionals and tendency causal claims are closely related (e.g., Frosch and Byrne, 2012), but despite these connections, they are usually studied separately. A unifying framework could consist in their dependence on probabilistic factors such as high conditional probability and statistical relevance (e.g., Adams, 1975; Eells, 1991; Douven, 2008, 2015). This paper presents a comparative empirical study on differences between judgments on tendency causal claims and indicative conditionals, how these judgments are driven by probabilistic factors, and (...) how these factors differ in their predictive power for both causal and conditional claims. (shrink)

In my thesis, I develop two distinct themes. The first part of my thesis is devoted to indicative conditionals and approaching them from an empirically informed perspective. In the second part, I am developing classical topics of philosophy of science, specifically, scientific objectivity and the role of values in science, in connection to recent methodological developments, revolving around the Replication Crisis.

To what use can causal claims established in good policy studies be put? We isolate two reasons inferences from study to target fail. First, policy variables do not produce results on their own; they need helping factors. The distribution of helping factors is likely to be unique or local for each study, so one cannot expect external validity to be all that common. Second, researchers often give too concrete a description of the cause in the study for it (...) to carry over to the target. Abstraction is necessary to get causes that travel. There is no sure-fire way to guard against these problems. But the unavailability of one perfect tool does not imply there are no second best contrivances. Two general pointers for Good Practice in policy advice follow from our diagnosis: focus on the concrete details in the target and use cross discipline heuristics that diversify background knowledge.¿Qué uso podemos hacer de las tesis causales que encontramos en los buenos estudios sobre política aplicada? Distinguimos dos razones por las que pueden fallar las inferencias desde la población en el estudio a la población general. En primer lugar, las variables que usamos en política no generan resultados por sí solas. Necesitan factores coadyuvantes. La distribución de estos factores es probablemente única o local en cada estudio, así que no hay motivos para esperar su validez externa. En segundo lugar, los investigadores a menudo dan descripciones demasiado concretas de la causalidad en el estudio como para poder generalizarlas. La abstracción es necesaria para obtener causas que viajen. No hay ningún modo absolutamente seguro de evitar semejantes fallos. Pero esto no implica que no haya arreglos subóptimos. De nuestro diagnóstico se siguen dos orientaciones generales sobre las buenas prácticas en la asesoría política: concentrémonos en los detalles concretos de la población en la que pretendemos intervenir y usemos heurísticas transdisciplinares que diversifiquen nuestro conocimiento de fondo. (shrink)

To what use can causal claims established in good studies be put? We give examples of studies from which inaccurate inferences were made about target policy situations. The usual diagnosis is that the studies in question lack external validity, which means that the same results do not hold in the target as in study. That’s a label that just repeats what we already knew. We offer a deeper analysis. Our analysis points to the need for interdisciplinarity and to (...) the demand to focus not on the study – as the expression ‘external validity’ invites you to do – but on the target. The call for interdisciplinary approaches to real life problems is common since it is widely acknowledged that what happens in the real world seldom falls under the auspices of any single research domain. Our focus is on one specific real life problem: how to use causal claims from good studies to help predict whether the policies tested will work in a new situation. Our analysis of what it takes to back up these predictions points up very specific stages in the process of prediction where we are bound to get it wrong if we do not diversify our concepts, our knowledge and our methods. We isolate two reasons inferences from study to target fail. First, policy variables do not produce results on their own; they need helping factors. The distribution of helping factors is likely to be unique or local for each study, so one cannot expect external validity to be all that common. Second, researchers often give too concrete a description of the cause in the study for it to carry over to the target.ion is necessary to get causes that travel. There is no sure-fire way to guard against these problems. But the unavailability of one perfect tool does not imply there are no second best contrivances. Two general pointers for Good Practice in policy advice follow from our diagnosis: focus on the concrete details in the target and use cross discipline heuristics that diversify background knowledge. (shrink)

To what use can causal claims established in good studies be put? We give examples of studies from which inaccurate inferences were made about target policy situations. The usual diagnosis is that the studies in question lack external validity, which means that the same results do not hold in the target as in study. That’s a label that just repeats what we already knew. We offer a deeper analysis. Our analysis points to the need for interdisciplinarity and to (...) the demand to focus not on the study – as the expression ‘external validity’ invites you to do – but on the target. The call for interdisciplinary approaches to real life problems is common since it is widely acknowledged that what happens in the real world seldom falls under the auspices of any single research domain. Our focus is on one specific real life problem: how to use causal claims from good studies to help predict whether the policies tested will work in a new situation. Our analysis of what it takes to back up these predictions points up very specific stages in the process of prediction where we are bound to get it wrong if we do not diversify our concepts, our knowledge and our methods. We isolate two reasons inferences from study to target fail. First, policy variables do not produce results on their own; they need helping factors. The distribution of helping factors is likely to be unique or local for each study, so one cannot expect external validity to be all that common. Second, researchers often give too concrete a description of the cause in the study for it to carry over to the target.ion is necessary to get causes that travel. There is no sure-fire way to guard against these problems. But the unavailability of one perfect tool does not imply there are no second best contrivances. Two general pointers for Good Practice in policy advice follow from our diagnosis: focus on the concrete details in the target and use cross discipline heuristics that diversify background knowledge. (shrink)

This article investigates how Causal Contextualism applies in a medical context. It is shown how the correct interpretation of some medical causal claims depends on relevant alternatives and then argued that these relevant alternatives are determined by standards of practice and practical limitations (of equipment, personnel, expertise, cost), amongst other factors. Causal Contextualism has recently been defended by a number of philosophers; however details of the relevant factors determining content in different contexts have been lacking. It (...) seems to me that establishing such details of Causal Contextualism goes a long way towards making the view plausible, and is also necessary for discovering some important consequences of Causal Contextualism. (shrink)

There are several supposedly lethal objections to the view that causation is essentially proportional. The first targets an account of proportionality in terms of causal models, pointing out that proportionality is too easily satisfied in causal model accounts of causation through manipulation of the range of values that a variable can take (Franklin-Hall, 2016). The second argues that proportionality legitimizes only the most general things as causes, and proportionality thereby contravenes causal intuitions (Bontly, 2005; Franklin-Hall, 2016; McDonnell, (...) 2018, 2017; Weslake, 2013). The final, and perhaps most intractable, objection holds that proportionality counter-intuitively legitimizes disjunctive causes (Shapiro and Sober, 2012; Weslake, 2017; Woodward, 2018). This paper provides a unified response to these objections, which is best formulated in a causal model framework. I first articulate two independently plausible principles of variable selection – exclusivity and exhaustivity. I then show how the adoption of these principles responds to Franklin-Hall’s objection, and dissolves the remaining two. (shrink)

How can Hume account for the meaning of causal claims? The causal realist, I argue, is, on Hume's view, saying something nonsensical. I argue that both realist and agnostic interpretations of Hume are inconsistent with his view of language and intentionality. But what then accounts for this illusion of meaning? And even when we use causal terms in accordance with Hume’s definitions, we seem merely to be making disguised self-reports. I argue that Hume’s view is not (...) as implausible as it sounds by exploring his conception of language. (shrink)

This paper contributes to two topics that have received insufficient attention in science and technology studies: the social dimensions of causal reasoning and how the knowledge-making site of expert testimony affects the production and reception of social scientific knowledge. It deals with how social scientists make causal claims when testifying as expert witnesses in trials where causal claims are relevant, using as a case study the so-called L’Aquila trial, in which experts were summoned by the (...) parties to testify on the causes of risk-related behavior by the inhabitants of an Italian city in early 2009. In particular, I analyze the process of selection of causal loci, the attribution and removal of agency, the increase and decrease of causal factors in the explanation of an event, and the delimitation of the explanandum. As a general insight derived from the case, it is argued that the position of experts in a trial––which side summons them––may be a more important factor than their branch of expertise to account for certain types of these practices. (shrink)

Even given agreement on the totality of conditions that brought about an effect, there often is disagreement about the cause of the effect, for example, the disagreement about the cause of the Gulf oil spill. Different conditions’ being deemed responsible accounts for such disagreements. The defense of the act of deeming a condition responsible often depends on showing that the condition was the appropriate target of interference in order to have avoided the effect.

Political science research, particularly in international relations and comparative politics, has increasingly become dominated by statistical and formal approaches. The promise of these approaches shifted the methodological emphasis away from case study research. In response, supporters of case study research argue that case studies provide evidence for causal claims that is not available through statistical and formal research methods, and many have advocated multimethod research. I propose a way of understanding the integration of multiple methodologies in which the (...) causes sought in case studies are treated as singular causation and contingent on a theoretical framework. (shrink)

According to Russo and Williamson (Int Stud Philos Sci 21(2):157–170, 2007, Hist Philos Life Sci 33:389–396, 2011a, Philos Sci 1(1):47–69, 2011b ), in order to establish a causal claim of the form, ‘_C_ is a cause of _E_’, one typically needs evidence that there is an underlying mechanism between _C_ and _E_ as well as evidence that _C_ makes a difference to _E_. This thesis has been used to argue that hierarchies of evidence, as championed by evidence-based movements, tend (...) to give primacy to evidence of difference making over evidence of mechanisms and are flawed because the two sorts of evidence are required and they should be treated on a par. An alternative approach gives primacy to evidence of mechanism over evidence of difference making. In this paper, we argue that this alternative approach is equally flawed, again because both sorts of evidence need to be treated on a par. As an illustration of this parity, we explain how scientists working in the ‘EnviroGenomarkers’ project constantly make use of the two evidential components in a dynamic and intertwined way. We argue that such an interplay is needed not only for causal assessment but also for policy purposes. (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.

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.

Some philosophers of physics recently expressed their skepticism about causation (Norton 2003b, 2007). However, this is not new. The view that causation does not refer to any ontological category perhaps can be attributed to Hume, Kant and Russell. On the other hand, some philosophers (Wesley Salmon and Phil Dowe) view causation as a physical process and some others (Cartwright) view causation as making claims about capacities possessed by objects. The issue about the ontological status of causal claims (...) involves issues concerning the ontological status of capacity, modality and dispositional claims. In this paper, my goal is to show that without engaging metaphysical debates about the ontological status of causal claims, it can be shown that we can objectively assign truth values to these statements. I argue that for causal claims to be objective we don't need to postulate the existence of special facts (specific to causal claims) in addition to ordinary physical facts described by physical theories. This, I think, is enough to justify the usefulness of this concept in certain branches (may be all) of science. Once this is achieved, there is no need to engage in unnecessary metaphysical debates. So, even if advanced physical theories don't mention this notion, causal reasoning can still be important in understanding the world not in the sense that science discovers special ontological category called causation but in the sense that we come to know certain facts about the world. (shrink)

In their recent book, Is Inequality Bad for Our Health?, Daniels, Kennedy, and Kawachi claim that to “act justly in health policy, we must have knowledge about the causal pathways through which socioeconomic (and other) inequalities work to produce differential health outcomes.” One of the central problems with this approach is its dependency on “knowledge about the causal pathways.” A widely held belief is that the randomized clinical trial (RCT) is, and ought to be the “gold standard” of (...) evaluating the causal efficacy of interventions. However, often the only data available are non-experimental, observational data. For such data, the necessary randomization is missing. Because the randomization is missing, it seems to follow that it is not possible to make epistemically warranted claims about the causal pathways. Although we are not sanguine about the difficulty in using observational data to make warranted causal claims, we are not as pessimistic as those who believe that the only warranted causal claims are claims based on data from (idealized) RCTs. We argue that careful, thoughtful study design, informed by expert knowledge, that incorporates propensity score matching methods in conjunction with instrumental variable analyses, provides the possibility of warranted causal claims using observational data. (shrink)

To claim that x was the cause of y is 1) to assume that x was one of a number of things, each of which together with the others was sufficient to have brought about y, and 2) to deem x responsible for the occurrence of y. A best-explanation argument, including application to cases, is offered in defense of this analysis, which holds that claiming that something is the cause is, in part, a speech act that reflects the cause selector’s (...) values or perspectives. No proposed alternative explanation accounts for all the cases with which I am familiar, but this analysis does account for them. Thus the analysis and the defense of sole singular causal claims call for more than empirical evidence, though of course evidence is very important. (shrink)

Causal claims are context sensitive. According to the old orthodoxy (Mackie 1974, Lewis 1986, inter alia), the context sensitivity of causal claims is all due to conversational pragmatics. According to the new contextualists (Hitchcock 1996, Woodward 2003, Maslen 2004, Menzies 2004, Schaffer 2005, and Hall ms), at least some of the context sensitivity of causal claims is semantic in nature. I want to discuss the prospects for causal contextualism, by asking why causal (...) claims are context sensitive, what they are sensitive to, and where they are sensitive to it. (shrink)

Causation appears to present us with an interpretative difficulty. While arguably a redundant relation given fundamental physics, it is nevertheless apparently pragmatically indispensable. This chapter revisits certain arguments made previously by the author for these claims with the benefit of hindsight, starting with the role of causal models in the human sciences, and attempting to explain why it is not possible to straightforwardly ground such models in fundamental physics. This suggests that further constraints, going beyond physics, are needed (...) to legitimate such models. These supplementary constraints could be reified, but that would seem to conflict with the completeness of physics. A response is to emphasize the practical role of causal talk, and the author suggests that a fictionalist approach might be worth exploring. After clarifying fictionalism as a general approach, they carry out in some detail the project of clarifying what a fictionalist attitude to causation would involve. (shrink)

Following Dretske (1977), there has been a considerable body of literature on the role of contrastive stress in causal claims. Following van Fraassen (1980), there has been a considerable body of literature on the role of contrastive stress in explanations and explanation-requesting why-questions. Amazingly, the two bodies of literature have remained almost entirely disjoint. With an understanding of the contrastive nature of ordinary causal claims, and of the linguistic roles of contrastive stress, it is possible to (...) provide a unified account of both phenomena. I provide such an account from within the framework of a probabilistic theory of causation. Relations of screening-off, long familiar to researchers in probabilistic causality, play a central role in this account. (shrink)

In this contribution, I explore the possibility of characterizing the emergence of time in causal set theory (CST) in terms of the growing block universe (GBU) metaphysics. I show that although GBU seems to be the most intuitive time metaphysics for CST, it leaves us with a number of interpretation problems, independently of which dynamics we choose to favor for the theory —here I shall consider the Classical Sequential Growth and the Covariant model. Discrete general covariance of the CSG (...) dynamics does not allow us to individuate a single history of the universe (defined by a causal history of different causal sets), thereby making the claim that ‘the past exists’ at best problematic. In addition, because the evolution of the universe in CSG dynamics leads to an outward branching causal tree, it becomes impossible to determine a proper ‘line of becoming’, thereby blurring the presentists’ claim that only the present exists. Similarly, the covariant approach runs into the same, if not even more severe problems, since each configuration of the universe would amount to a set of possible causal sets, thereby making the individuation of a single configuration of the universe —and thus the physical interpretation of the theory— implausible. (shrink)

We welcome Norenzayan et al.’s claim that the prosocial effects of beliefs in supernatural agents extend beyond Big Gods. To date, however, supporting evidence has focused on the Abrahamic Big God, making generalisations difficult. We discuss a recent study that highlights the need for clarity about the causal path by which supernatural beliefs affect the evolution of big societies.

Are causal explanations (e.g., “she switched careers because of the COVID pandemic”) treated differently from the corresponding claims that one factor caused another (e.g., “the COVID pandemic caused her to switch careers”)? We examined whether explanatory and causal claims diverge in their responsiveness to two different types of information: covariation strength and mechanism information. We report five experiments with 1,730 participants total, showing that compared to judgments of causal strength, explanatory judgments tend to bemoresensitive to (...) mechanism andlesssensitive to covariation – even though explanatory judgments respond to both types of information. We also report exploratory comparisons to judgments of understanding, and discuss implications of our findings for theories of explanation, understanding, and causal attribution. These findings shed light on the potentially unique role of explanation in cognition. (shrink)

Causal claims in biomedical contexts are ubiquitous albeit they are not always made explicit. This paper addresses the question of what causal claims mean in the context of disease. It is argued that in medical contexts causality ought to be interpreted according to the epistemic theory. The epistemic theory offers an alternative to traditional accounts that cash out causation either in terms of “difference-making” relations or in terms of mechanisms. According to the epistemic approach, causal (...) claims tell us about which inferences (e.g., diagnoses and prognoses) are appropriate, rather than about the presence of some physical causal relation analogous to distance or gravitational attraction. It is shown that the epistemic theory has important consequences for medical practice, in particular with regard to evidence-based causal assessment. (shrink)

Causal theories of action, perception and knowledge are each beset by problems of so-called ‘deviant’ causal chains. For each such theory, counterexamples are formed using odd or co-incidental causal chains to establish that the theory is committed to unpalatable claims about some intentional action, about a case of veridical perception or about the acquisition of genuine knowledge. In this paper I will argue that three well-known examples of a deviant causal chain have something in common: (...) they each violate Yablos proportionality constraint on causation. I will argue that this constraint provides the key to saving causal theories from deviant chains. (shrink)

We argue that the health sciences make causal claims on the basis of evidence both of physical mechanisms, and of probabilistic dependencies. Consequently, an analysis of causality solely in terms of physical mechanisms or solely in terms of probabilistic relationships, does not do justice to the causal claims of these sciences. Yet there seems to be a single relation of cause in these sciences - pluralism about causality will not do either. Instead, we maintain, the health (...) sciences require a theory of causality that unifies its mechanistic and probabilistic aspects. We argue that the epistemic theory of causality provides the required unification. (shrink)

After the U.S. dropped atomic bombs on Hiroshima and Nagasaki in Japan in 1945, both nations experienced a profound need for a new and encompassing story of what it meant to be Japanese, and to be American, in the permanent nuclear age. This article is a thought experiment to juxtapose the writings and personas of two people who helped their respective societies answer those needs and questions during the early Cold War: Takashi Nagai—medical radiologist, and survivor of the American atomic (...) bombing of Nagasaki, and Albert Wohlstetter—leading American civilian nuclear strategist for the RAND Corporation in the 1950s. Using a combination of mythopeic analysis, biography and illuminative juxtaposition, the study discovers surprising similarities and analogies between the two cases. They each enact and propose interesting variations of sacrificial causalities—claims that human nuclear sacrifices past or promised can bring peace by deterrence now or peace by abolition soon enough. This is an important study now, as both Japan’s nuclear pacifism and the American nuclear umbrella in Northeast Asia are coming under more severe questioning than perhaps ever before. (shrink)

Nancy Cartwright begins her recent book, Hunting Causes and Using Them, by noting that while a few years ago real causal claims were in dispute, nowadays “causality is back, and with a vengeance.” In the case of the social sciences, Keith Morrison writes that “Social science asks ‘why?’. Detecting causality or its corollary—prediction—is the jewel in the crown of social science research.” With respect to the health sciences, Judea Pearl writes that the “research questions that motivate most studies (...) in the health sciences are causal in nature.” However, not all data used by people interested in making causal claims come from experiments that use random assignment to control and treatment groups. Indeed, much research in the social and health science depends on non-experimental, observational data. Thus, one of the most important problems in the social and health sciences concerns making warranted causal claims using non-experimental, observational data; viz., “Can observational data be used to make etiological inferences leading to warranted causal claims?” This paper examines one method of warranting causal claims that is especially widespread in epidemiology and the health sciences generally—the use of causal criteria. It is argued that cases of complex causation generally, and redundant causation—both causal overdetermination and causal preemption—specifically, undermine the use of such criteria to warrant causal claims. (shrink)

In The Biopsychosocial Model of Health and Disease, Derek Bolton and Grant Gillett argue that a defensible updated version of the biopsychosocial model requires a metaphysically adequate account of disease causation that can accommodate biological, psychological, and social factors. This present paper offers a philosophical critique of their account of biopsychosocial causation. I argue that their account relies on claims about the normativity and the semantic content of biological information that are metaphysically contentious. Moreover, I suggest that these (...) class='Hi'>claims are unnecessary for a defence of biopsychosocial causation, as the roles of multiple and diverse factors in disease causation can be readily accommodated by a more widely accepted and less metaphysically contentious account of causation. I then raise the more general concern that they are misdiagnosing the problem with the traditional version of the biopsychosocial model. The challenge when developing an explanatorily valuable version of the biopsychosocial model, I argue, is not so much providing an adequate account of biopsychosocial causation, but providing an adequate account of causal selection. Finally, I consider how this problem may be solved to arrive at a more explanatorily valuable and clinically useful version of the biopsychosocial model. (shrink)

According to the causal-historical theory of reference, natural kind terms refer in virtue of complicated causal relations the speakers have to their environment. A common objection to the theory is that purely causal relations are insufficient to fix reference in a determinate fashion. The so-called hybrid view holds that what is also needed for successful fixing are true descriptions associated in the mind of the speaker with the referent. The main claim of this paper is that the (...) objection fails: reference fixing of natural kind terms can be purely causal. The main argument draws inspiration from recent theoretical advances made in metaphysics of kinds by Marion Godman, Antonella Mallozzi, and David Papineau. The main claim is that their notion of super-explanatory properties may explain how reference of many kind terms can be fixed purely causally. (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)

This paper proposes that the relation between property instances and token causal powers is akin to the relation between primary substances and property instances on the Aristotelian account of property instantiation. This view permits an individual to have two tokens of the same type of causal power. Paul Audi has argued that this cannot be: two tokens of the same power type are discernible, he claims, only if they are borne by discernible individuals. In the context of (...) this criticism, he proposes that power tokens are individuated in part by the individuals bearing them. This paper responds to Audi’s criticism and argues against his preferred view. This paper thus establishes a theory of token causal powers where previously the literature contained only a presumed view and Audi’s objections. (shrink)

The first part of this paper presents an argument showing that the currently most highly acclaimed interventionist theory of causation, i.e. the one advanced by Woodward, excludes supervening macro properties from having a causal influence on effects of their micro supervenience bases. Moreover, this interventionist exclusion argument is demonstrated to rest on weaker premises than classical exclusion arguments. The second part then discusses a weakening of interventionism that Woodward suggests. This weakened version of interventionism turns out either to be (...) inapplicable to cases of downward causation involving supervening macro properties or to render corresponding causal claims meaningless. In sum, the paper argues that, contrary to what many non-reductive physicalists claim, interventionism does not render non-reductive physicalism immune to the problem of causal exclusion. (shrink)

Social research, from economics to demography and epidemiology, makes extensive use of statistical models in order to establish causal relations. The question arises as to what guarantees the causal interpretation of such models. In this paper we focus on econometrics and advance the view that causal models are ‘augmented’ statistical models that incorporate important causal information which contributes to their causal interpretation. The primary objective of this paper is to argue that causal claims (...) are established on the basis of a plurality of evidence. We discuss the consequences of ‘evidential pluralism’ in the context of econometric modelling. (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)

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)