The explanatory role of natural selection is one of the long-term debates in evolutionary biology. Nevertheless, the consensus has been slippery because conceptual confusions and the absence of a unified, formal causal model that integrates different explanatory scopes of natural selection. In this study we attempt to examine two questions: (i) What can the theory of natural selection explain? and (ii) Is there a causal or explanatory model that integrates all natural selection explananda? For the first question, we argue that (...) five explananda have been assigned to the theory of natural selection and that four of them may be actually considered explananda of natural selection. For the second question, we claim that a probabilistic conception of causality and the statistical relevance concept of explanation are both good models for understanding the explanatory role of natural selection. We review the biological and philosophical disputes about the explanatory role of natural selection and formalize some explananda in probabilistic terms using classical results from population genetics. Most of these explananda have been discussed in philosophical terms but some of them have been mixed up and confused. We analyze and set the limits of these problems. (shrink)

This paper examines a promising probabilistic theory of singular causation developed by David Lewis. I argue that Lewis' theory must be made more sophisticated to deal with certain counterexamples involving pre-emption. These counterexamples appear to show that in the usual case singular causation requires an unbroken causal process to link cause with effect. I propose a new probabilistic account of singular causation, within the framework developed by Lewis, which captures this intuition.

This article introduces the notion of the directedness of a process, which underlies event causation as well as the persistence of things. Using this notion it investigates what happens in typical cases of active event causation. Causes never necessitate their effects because even non- probabilistic causes can be counteracted.

Actual causes - e.g. Suzy's being exposed to asbestos - often bring about their effects - e.g. Suzy's suffering mesothelioma - probabilistically. I use probabilistic causal models to tackle one of the thornier difficulties for traditional accounts of probabilistic actual causation: namely probabilistic preemption.

The starting point in the development of probabilistic analyses of token causation has usually been the naïve intuition that, in some relevant sense, a cause raises the probability of its effect. But there are well-known examples both of non-probability-raising causation and of probability-raising non-causation. Sophisticated extant probabilistic analyses treat many such cases correctly, but only at the cost of excluding the possibilities of direct non-probability-raising causation, failures of causal transitivity, action-at-a-distance, prevention, and causation (...) by absence and omission. I show that an examination of the structure of these problem cases suggests a different treatment, one which avoids the costs of extant probabilistic analyses. (shrink)

ABSTRACT Joseph Halpern and Judea Pearl draw upon structural equation models to develop an attractive analysis of ‘actual cause’. Their analysis is designed for the case of deterministic causation. I show that their account can be naturally extended to provide an elegant treatment of probabilistic causation. 1Introduction 2Preemption 3Structural Equation Models 4The Halpern and Pearl Definition of ‘Actual Cause’ 5Preemption Again 6The Probabilistic Case 7Probabilistic Causal Models 8A Proposed Probabilistic Extension of Halpern and Pearl’s Definition (...) 9Twardy and Korb’s Account 10Probabilistic Fizzling 11Conclusion. (shrink)

It is argued in this paper that although much attention has been paid to causal chains and common causes within the literature on probabilistic causality, a primary virtue of that approach is its ability to deal with cases of multiple causation. In doing so some ways are indicated in which contemporary sine qua non analyses of causation are too narrow (and ways in which probabilistic causality is not) and an argument by Reichenbach designed to provide a (...) basis for the asymmetry of causation is refined. The importance of referring causal claims to an abstract model is also emphasized. (shrink)

It has recently been remarked that the argument for physicalism from the causal closure of the physical is incomplete. It is only effective against mental causation manifested in the action of putative mental forces that lead to acceleration of particles in the nervous system. Based on consideration of anomalous, physically unaccounted-for correlations of neural events, I argue that irreducible mental causation whose nature is at least prima facie probabilistic is conceivable. The manifestation of such causation should (...) be accompanied by a local violation of the Second Law of thermodynamics. I claim that mental causation can be viewed as the disposition of mental states to alter the state probability distribution within the nervous system, with no violation of the conservation laws. If confirmed by neurophysical research, it would indicate a kind of causal homogeneity of the world. Causation would manifest probabilistically in both quantum mechanical and psychophysical systems, and the dynamics of both would be determined by the temporal evolution of the corresponding system state function. Finally, I contend that a probabilistic account of mental causation can consistently explain the character of selectional states that ensure the uniformity of causal patterns, as well as the fact that different physical realizers of a mental property cause the same physical effects in different contexts. (shrink)

Hume thought that if you believed in powers, you believed in necessary connections in nature. He was then able to argue that there were none such because anything could follow anything else. But Hume wrong-footed his opponents. A power does not necessitate its manifestations: rather, it disposes towards them in a way that is less than necessary but more than purely contingent. -/- In this paper a dispositional theory of causation is offered. Causes dispose towards their effects and often (...) produce them. But a set of causes, even though they may succeed in producing an effect, cannot necessitate it since the effect could have been counteracted by some additional power. This would require a separation of our concepts of causal production and causal necessitation. The most conspicuous cases of causation are those where powers accumulate and pass a requisite threshold for an effect to occur. -/- We develop a model for representing powers as constituent vectors within an n-dimensional quality space, where composition of causes appears as vector addition. Even our resultant vector, however, has to be understood as having dispositional force only. This model throws new light on causal modality and cases of prevention, causation by absence and probabilistic causation. (shrink)

Causation is a macroscopic phenomenon. The temporal asymmetry displayed by causation must somehow emerge along with other asymmetric macroscopic phenomena like entropy increase and the arrow of radiation. I shall approach this issue by considering ‘causal inference’ techniques that allow causal relations to be inferred from sets of observed correlations. I shall show that these techniques are best explained by a reduction of causation to structures of equations with probabilistically independent exogenous terms. This exogenous probabilistic independence (...) imposes a recursive order on these equations and a consequent distinction between dependent and independent variables that lines up with the temporal asymmetry of causation. (shrink)

In this paper, I argue that causal theories of memory are typically committed to two independent, non-mutually entailing theses. The first thesis pertains to the necessity of appropriate causation in memory, specifying a condition token memories need to satisfy. The second pertains to the explanation of memory reliability in causal terms and it concerns memory as a type of mental state. Post-causal theories of memory can reject only the first (weak post-causalism) or both (strong post-causalism) theses. Upon this backdrop, (...) I examine Werning’s (2020) causalist argument from probabilistic correlation. I argue that it doesn’t establish the necessity of appropriate causation and thus it can only target strong post-causalist theories. I end up by presenting some general considerations, suggesting that memories may not always be causally linked to past experiences. (shrink)

D. M. Armstrong famously claims that deterministic laws of nature are contingent relations between universals and that his account can also be straightforwardly extended to irreducibly probabilistic laws of nature. For the most part, philosophers have neglected to scrutinize Armstrong’s account of probabilistic laws. This is surprising precisely because his own claims about probabilistic laws make it unclear just what he takes them to be. We offer three interpretations of what Armstrong-style probabilistic laws are, and argue (...) that all three interpretations are incompatible either with some feature of Armstrong’s broader metaphysics or with essential features of his account of laws (or both). (shrink)

A key challenge for probabilistic causal models is to distinguish non-causal probabilistic dependencies from true causal relations. To accomplish this task, causal models are usually required to satisfy several constraints. Two prominent constraints are the causal Markov condition and the faithfulness condition. However, other constraints are also needed. One of these additional constraints is the causal sufficiency condition, which states that models must not omit any direct common causes of the variables they contain. In this paper, I argue (...) that the causal sufficiency condition is problematic: (1) it is incompatible with the requirement that the variables in a model must not stand in non-causal necessary dependence relations, such as mathematical or conceptual relations, or relations described in terms of supervenience or grounding, (2) it presupposes more causal knowledge as primitive than is actually needed to create adequate causal models, and (3) if models are only required to be causally sufficient, they cannot deal with cases where variables are probabilistically related by accident, such as Sober’s example of the relationship between bread prices in England and the sea level in Venice. I show that these problems can be avoided if causal models are required to be monotonic in the following sense: the causal relations occurring in a model M would not disappear if further variables were added to M. I give a definition of this monotonicity condition and conclude that causal models should be required to be monotonic rather than causally sufficient. (shrink)

Coherentism maintains that coherent beliefs are more likely to be true than incoherent beliefs, and that coherent evidence provides more confirmation of a hypothesis when the evidence is made coherent by the explanation provided by that hypothesis. Although probabilistic models of credence ought to be well-suited to justifying such claims, negative results from Bayesian epistemology have suggested otherwise. In this essay we argue that the connection between coherence and confirmation should be understood as a relation mediated by the causal (...) relationships among the evidence and a hypothesis, and we offer a framework for doing so by fitting together probabilistic models of coherence, confirmation, and causation. We show that the causal structure among the evidence and hypothesis is sometimes enough to determine whether the coherence of the evidence boosts confirmation of the hypothesis, makes no difference to it, or even reduces it. We also show that, ceteris paribus, it is not the coherence of the evidence that boosts confirmation, but rather the ratio of the coherence of the evidence to the coherence of the evidence conditional on a hypothesis. (shrink)

The search for causes of perinatal brain damage needs a solid theoretical foundation. Current theory apparently does not offer a unanimously accepted view of what constitutes a cause, and how it can be identified. We discuss nine potential theoretical misconceptions: (1) too narrow a view of what is a cause (causal production vs. facilitation), (2) extrapolating from possibility to fact (potential vs. factual causation), (3) if X, then invariably Y (determinism vs. probabilism), (4) co-occurrence in individuals vs. association in (...) populations, (5) one cause is all that is needed (single cause attribution vs. multicausal constellations), (6) drawing causal inferences from very small numbers of observations (the tendency to generalize), (7) unstated causal inferences, (8) ignoring heterogeneity, and (9) failing to consider alternative explanations for what is observed. We hope that our critical discussion will contribute to fruitful research and help reduce the burden of perinatal brain damage. (shrink)

There is a current debate about the extent to which Academic Freedom should be permitted in our universities. On the one hand, we have traditionalists who maintain that Academic Freedom should be unrestricted: people who have the appropriate qualifications and accomplishments should be allowed to develop theories about how the world is, or ought to be, as they see fit. On the other hand, we have post-traditional philosophers who argue against this degree of Academic Freedom. I consider a conservative version (...) of post-traditional philosophy that permits restrictions on Academic Freedom only if the following conditions are met, Condition 1: The dissemination of the results of a given research project R must cause significant harm to some people, especially to people from oppressed groups. Condition 2: Condition 1 must possess strong empirical support, and which accepts the following assumptions: (1) there is a world of objective facts that is, in principle, discoverable, (2) rational means are the means of discovering it and, (3) rational means requires strong empirical support. I define strong empirical support for an hypothesis h on evidence e in probabilistic terms, as a ratio of posterior to prior probabilities substantially exceeding 1. I now argue in favour of a research policy that accepts unrestricted Academic Freedom. My argument is that there is a formal and general quandary that arises within the standard theory of probability when we apply this account of empirical support to a set of possible causal hypotheses framed in such a way that the “reverse probabilities”, pr(e/h) are 1. I consider various possible ways to escape this quandary, none of which are without difficulties, concluding that a research policy allowing for unrestricted Academic Freedom is probably the best that we can hope for. (shrink)

Stephen Yablo’s notion of proportionality, despite controversies surrounding it, has played a significant role in philosophical discussions of mental causation and of high-level causation more generally. In particular, it is invoked in James Woodward’s interventionist account of high-level causation and explanation, and is implicit in a novel approach to constructing variables for causal modeling in the machine learning literature, known as causal feature learning (CFL). In this article, we articulate an account of proportionality inspired by both Yablo’s (...) account of proportionality and the CFL account of variable construction. The resulting account has at least three merits. First, it illuminates an important feature of the notion of proportionality, when it is adapted to a probabilistic and interventionist framework. The feature is that at the center of the notion of proportionality lies the concept of “determinate intervention effects.” Second, it makes manifest a virtue of (common types of) high-level causal/explanatory statements over low-level ones, when relevant intervention effects are determinate. Third, it overcomes a limitation of the CFL framework and thereby also addresses a challenge to interventionist accounts of high-level causation. (shrink)

Is the common cause principle merely one of a set of useful heuristics for discovering causal relations, or is it rather a piece of heavy duty metaphysics, capable of grounding the direction of causation itself? Since the principle was introduced in Reichenbach’s groundbreaking work The Direction of Time (1956), there have been a series of attempts to pursue the latter program—to take the probabilistic relationships constitutive of the principle of the common cause and use them to ground the (...) direction of causation. These attempts have not all explicitly appealed to the principle as originally formulated; it has also appeared in the guise of independence conditions, counterfactual overdetermination, and, in the causal modelling literature, as the causal markov condition. In this paper, I identify a set of difficulties for grounding the asymmetry of causation on the principle and its descendents. The first difficulty, concerning what I call the vertical placement of causation, consists of a tension between considerations that drive towards the macroscopic scale, and considerations that drive towards the microscopic scale—the worry is that these considerations cannot both be comfortably accommodated. The second difficulty consists of a novel potential counterexample to the principle based on the familiar Einstein Podolsky Rosen (EPR) correlations in quantum mechanics. (shrink)

Many tasks in statistical and causal inference can be construed as problems of entailment in a suitable formal language. We ask whether those problems are more difficult, from a computational perspective, for causal probabilistic languages than for pure probabilistic (or “associational”) languages. Despite several senses in which causal reasoning is indeed more complex—both expressively and inferentially—we show that causal entailment (or satisfiability) problems can be systematically and robustly reduced to purely probabilistic problems. Thus there is no jump (...) in computational complexity. Along the way we answer several open problems concerning the complexity of well-known probability logics, in particular demonstrating the ${\exists \mathbb {R}}$ -completeness of a polynomial probability calculus, as well as a seemingly much simpler system, the logic of comparative conditional probability. (shrink)

The problem of indeterminism in quantum mechanics usually being considered as a generalization determinism of classical mechanics and physics for the case of discrete (quantum) changes is interpreted as an only mathematical problem referring to the relation of a set of independent choices to a well-ordered series therefore regulated by the equivalence of the axiom of choice and the well-ordering “theorem”. The former corresponds to quantum indeterminism, and the latter, to classical determinism. No other premises (besides the above only mathematical (...) equivalence) are necessary to explain how the probabilistic causation of quantum mechanics refers to the unambiguous determinism of classical physics. The same equivalence underlies the mathematical formalism of quantum mechanics. It merged the well-ordered components of the vectors of Heisenberg’s matrix mechanics and the non-ordered members of the wave functions of Schrödinger’s undulatory mechanics. The mathematical condition of that merging is just the equivalence of the axiom of choice and the well-ordering theorem implying in turn Max Born’s probabilistic interpretation of quantum mechanics. Particularly, energy conservation is justified differently than classical physics. It is due to the equivalence at issue rather than to the principle of least action. One may involve two forms of energy conservation corresponding whether to the smooth changes of classical physics or to the discrete changes of quantum mechanics. Further both kinds of changes can be equated to each other under the unified energy conservation as well as the conditions for the violation of energy conservation to be investigated therefore directing to a certain generalization of energy conservation. (shrink)

The following paper is a panpsychist metaphysics and seeks to avoid any radical emergence of mentality. Science has progressed by stripping the world of all mental qualities but a complete understanding of the world must ultimately put these back. The two types of mental qualities that must be reinstated as fundamentals are the private worlds of individual subjects and phenomenal qualities like colors. I view these as separate aspects of mind although they have a history of being conflated. In this (...) proposal qualities or proto-qualities exist in the public domain and are not restricted to or fenced into the private minds of subjects. Thus there is a fundamental dualism as both private efficacious conscious observers and publically observable phenomenal qualities exist as natural entities at the most basic level. -/- The following is a simple model example: Suppose electrons of certain vibrational energy levels always radiate the color blue while others of different vibrational energy levels radiate different colors thereby expressing universal psychophysical bridging laws. Electrons are mental subjects and within an atom can all perceive each other and orchestrate their actions to create the probabilistic atomic structure. The psychophysical laws that cause generation of phenomenal qualities as public objects are universal and the qualities are the identical ones that are observed by the higher level subjects. -/- In this model all minds at lower and higher levels are fundamental particles that have causal efficacy. The difference is the lowest level particles move themselves by influencing the direction and timing of their own quantum jumps while the higher level minds influence the probability distribution of the quantum jumps of lower level ones. Unlike Leibnizian monads these monads have windows and can really view each other and interact. Then the world can be seen as a communication network of interacting individuals each broadcasting and receiving information in a universal language of phenomenal qualities. (shrink)

A common objection to probabilistic theories of causation is that there are prima facie causes that lower the probability of their effects. Among the many replies to this objection, little attention has been given to Mellor's (1995) indirect strategy to deny that probability-lowering factors are bona fide causes. According to Mellor, such factors do not satisfy the evidential, explanatory, and instrumental connotations of causation. The paper argues that the evidential connotation only entails an epistemically relativized form of (...) causal attribution, not causation itself, and that there are clear cases of explanation and instrumental reasoning that must appeal to negatively relevant factors. In the end, it suggests a more liberal interpretation of causation that restores its connotations. Una objeción común a las teorías probabilísticas de la causalidad es que aparentemente existen causas que disminuyen la probabilidad de sus efectos. Entre las muchas respuestas a esta objeción, se le ha dado poca atención a la estrategia indirecta de D. H. Mellor (1995) para negar que un factor que disminuya la probabilidad de un efecto sea una causa legítima. Según Mellor, tales factores no satisfacen las connotaciones evidenciales, explicativas e instrumentales de la causalidad. El artículo argumenta que la connotación evidencial sólo implica una forma epistémicamente relativizada de atribución causal y no la causalidad misma, y que hay casos claros de explicación y razonamiento instrumental que deben apelar a factores negativamente relevantes. Se sugiere una interpretación más liberal de la causalidad que reinstaura sus connotaciones. (shrink)

A cause may influence its effect via multiple paths. Paradigmatically (Hesslow [1974]), taking birth control pills both decreases one’s risk of thrombosis by preventing pregnancy and increases it by producing a blood chemical. Building on Pearl ([2001]), I explicate the notion of a path-specific effect. Roughly, a path-specific effect of C on E via path P is the degree to which a change in C would change E were they to be transmitted only via P. Facts about such effects may (...) be gleaned from the structural equations commonly used to represent the causal relationships among variables. I contrast my analysis of the Hesslow case with those given by theorists of probabilistic causality, who mistakenly link it to issues of causal heterogeneity, token-causation and indeterminism. The reason probabilistic theories misdiagnose this case is that they pay inadequate attention to the structural relationships among variables. (shrink)

The goal of this paper is to argue that there is indeterminacy in causation. I present three types of cases in which it is indeterminate whether an event c caused another event e: (1) cases of absence causation recently discussed by Bernstein and by Swanson, (2) cases leading to Sorites paradoxes for causation, and (3) cases where c and e occur in certain indeterministic causal structures and it is therefore indeterminate whether there is a causal relation between (...) them. These cases, I argue, provide strong evidence that indeterminacy is an important general feature of the causal relation and that philosophical theorizing about causation should take this observation into account. (shrink)

While agreeing with Fischborn’s (2018) contention that, according to one traditional definition of compatibilism, my position should be classified as that of a libertarian incompatibilist, I argue here for a different view of compatibilism. This view involves, on the one hand, local probabilistic causation of decisions (rather than universal strict determinism) and, on the other, free will conceived as involving decisions generated by a decision-making process carried out by the brain, which consciously contemplates different alternatives and could in (...) principle have been different from what it was, implying that the agent could in normal conditions have done otherwise in the same circumstances. After discussing different views of causation (including determinism) and of free will, I make a revision of some passages from my earlier work, quoted by Fischborn. I conclude that what is crucial in the question of (in)compatibilism is the (in)compatibility between freedom of decisions and natural causation of human actions. According to this looser and, I argue, more pertinent view of compatibilism, I maintain my previous classification of my position on the matter as compatibilist. (shrink)

Existing research suggests that people's judgments of actual causation can be influenced by the degree to which they regard certain events as normal. We develop an explanation for this phenomenon that draws on standard tools from the literature on graphical causal models and, in particular, on the idea of probabilistic sampling. Using these tools, we propose a new measure of actual causal strength. This measure accurately captures three effects of normality on causal judgment that have been observed in (...) existing studies. More importantly, the measure predicts a new effect ("abnormal deflation"). Two studies show that people's judgments do, in fact, show this new effect. Taken together, the patterns of people's causal judgments thereby provide support for the proposed explanation. (shrink)

This book argues that there is a joint in nature between seeing and thinking, perception, and cognition. Perception is constitutively iconic, nonconceptual, and nonpropositional, whereas cognition does not have these properties constitutively. The book does not appeal to “intuitions,” as is common in philosophy, but to empirical evidence, including experiments in neuroscience and psychology. The book argues that cognition affects perception, i.e., that perception is cognitively penetrable, but that this does not impugn the joint in nature. A key part of (...) the argument is that we perceive not only low-level properties like colors, shapes, and textures but also high-level properties such as faces and causation. Along the way, the book explains the difference between perception and perceptual memory, the differences between format and content, and whether perception is probabilistic despite our lack of awareness of probabilistic properties. The book argues for perceptual categories that are not concepts, that perception need not be singular, that perceptual attribution and perceptual discrimination are equally fundamental, and that basic features of the mind known as “core cognition” are not a third category in between perception and cognition. The chapter on consciousness leverages these results to argue against some of the most widely accepted theories of consciousness. Although only one chapter is about consciousness, much of the rest of the book repurposes work on consciousness to isolate the scientific basis of perception. (shrink)

In her ‘Causality and Determination’, Anscombe argues for the strong thesis that despite centuries of philosophical assumption to the contrary, the supposition that causality and necessity have something essential to do with one another is baseless. In this paper, I assess Anscombe’s arguments and endorse her conclusion. I then attempt to argue that her arguments remain highly relevant today, despite the fact that most popular general views of causation today are firmly probabilistic in orientation and thus show no (...) trace of the assumptions Anscombe hoped to undermine. My suggestion is that Anscombe's interests in causality are distinct from those which mostly animate the modern debate about the general nature of causality and that in those specialized areas of philosophy in which those concerns still dominate, one can still see the effects of the fallacies and confusions to which she alerts us. I conclude by offering two possible complementary explanations of the tendency to suppose that causation is a variety of necessitation. (shrink)

The concept of causation plays a central role in many philosophical theories, and yet no account of causation has gained widespread acceptance among those who have investigated its foundations. Theories based on laws, counterfactuals, physical processes, and probabilistic dependence and independence relations (the list is by no means exhaustive) have all received detailed treatment in recent years—and, while no account has been entirely successful, it is generally agreed that the concept has been greatly clarified by the attempts. (...) In this magnificent book, Woodward aims to give a unified account of causation and causal explanation in terms of the notion of a manipulation (or intervention, terms which can be read interchangeably). Not only does he produce in my view the most illuminating and comprehensive account of causation on offer, his theory also opens a great many avenues for future work in the area, and has ramifications for many other areas of philosophy. Making Things Happen ought to be of interest not only to philosophers of causation and philosophers of science, but to any philosopher whose concerns involve assumptions about the nature of causation, laws, or explanation. (shrink)

Decision-making typically requires judgments about causal relations: we need to know the causal effects of our actions and the causal relevance of various environmental factors. We investigate how several individuals' causal judgments can be aggregated into collective causal judgments. First, we consider the aggregation of causal judgments via the aggregation of probabilistic judgments, and identify the limitations of this approach. We then explore the possibility of aggregating causal judgments independently of probabilistic ones. Formally, we introduce the problem of (...) causal-network aggregation. Finally, we revisit the aggregation of probabilistic judgments when this is constrained by prior aggregation of qualitative causal judgments. (shrink)

Causal decision theory defines a rational action as the one that tends to cause the best outcomes. If we adopt counterfactual or probabilistic theories of causation, then we may face problems in overdetermination cases. Do such problems affect Causal decision theory? The aim of this work is to show that the concept of causation that has been fundamental in all versions of causal decision theory is not the most intuitive one. Since overdetermination poses problems for a counterfactual (...) theory of causation, one can think that a version of causal decision theory based on counterfactual dependence may also be vulnerable to such scenarios. However, only when an intuitive, not analyzed notion of causation is presupposed as a ground for a more plausible version of causal decision theory, overdetermination turns problematic. The first interesting consequence of this is that there are more reasons to dismiss traditional theories of causation (and to accept others). The second is to confirm that traditional causal decision theory is not based on our intuitive concept of the causal relation. (shrink)

Eyal Shahar’s essay review [1] of James Penston’s remarkable book [2] seems more inspired playful academic provocation than review or essay, expressing dramatic views of impossible validity. The account given of modern biostatistical causation reveals the slide from science into the intellectual confusion and non-science RCTs have created: “…. the purpose of medical research is to estimate the magnitude of the effect of a causal contrast, for example the probability ratio of a binary outcome …” But Shahar’s world is (...) simultaneously not probabilistic, but of absolute uncertainty: “We should have no confidence in any type of evidence ….. We should have no confidence at all”. Shahar’s "Causal contrast" is attractive. It seems to make sense, but bypasses in two words the means of establishing causation by the scientific method. This phrase assumes a numeric statistically significant “contrast” is causal rather than a potential correlation requiring further investigation. The concept of “causal contrast” is a slippery slope from sense into biostatistical non-science. This can be illustrated with an hypothetical RCT where 100% of interventions exhibit a posited treatment effect and 0% of placebo controls. Internal validity is seemingly quite reasonably assumed satisfied (common-sense dictating the likelihood of an awesome magnificent fraud, bias or plain error of the magnitude required is infinitesimal). Scientific method appears satisfied. The RCT demonstrates: (1) strict regularity of outcome in the presence of posited cause; (2) the absence of outcome in its absence and (3) an intervention (experiment) showing the direction of causation is from posited cause to posited effect. Now travel further down the slope from science. Assume 50% of interventions and 0% of controls are positive. We compromise scientific method, but justify this by assuming a large subgroup which we say surely must on these figures be exhibiting the posited treatment effect. But what of 10% of interventions and 9% of placebo controls exhibiting the posited treatment effect? Our biostatistician says the 1% “causal contrast” is statistically significant. But we have: (1) minimal evidence of regularity; (2) the posited outcome irrespective of presence of posited cause and (3) our intervention is at the highest equivocal in demonstrating any form of causation. This is not science. It is, however, where biostatistics has unthinkingly taken us, as Penston has shown comprehensively [2]. We, the audience of published medical research, are now for the 10% / 9% example well down the slope from science. An unattractive hypothesis results requiring numerous assumptions similar to these:- "There is a 'contrast' which is ‘causal’, albeit the method employed is not scientific. An effect of the intervention has been observed in a very small subgroup. This subgroup is susceptible to treatment. The similar number of placebo controls exhibiting the outcome sought is irrelevant, because the 1% difference between intervention and controls is statistically significant. The statistical analysis is valid and reliable. The RCT’s internal validity is sufficiently satisfied. No funding or bias or fraud has affected the results or their analysis.” As Penston notes: “Confirming and refuting the results of research is crucial to science …. But … there’s no way of testing the results of any particular large-scale RCT or epidemiological study. Each study … is left hanging in the air, unsupported.” It gets worse. To identify a rare serious adverse reaction of a frequency of 1:10,000 can require a trial of 200,000 or larger split between controls and interventions. This is not done. But for every 100 who prospectively benefit from the intervention, 9,900 also receive it. And for every 100 benefiting one person (who likely gains no benefit) will suffer a serious unidentified adverse reaction. This is also without taking account of more common adverse reactions whether serious or otherwise. References [1] Shahar, E. (2011) Research and medicine: human conjectures at every turn. International Journal of Person Centered Medicine 1 (2), 250-253. [2] Penston, J. (2010). stats.con: How we’ve been fooled by statistics-based research in medicine. London: The London Press, UK. (shrink)

Although there are several monistic and dualistic approaches to the mind-body problem on the basis of classical or quantum mechanics, thus far no consensus exists about a solution. Recently, the Elementary Process Theory (EPT) has been developed: this corresponds with a fundamentally new disciplinary matrix for the study of physical reality. The purpose of the present research was to investigate the mind-body problem within this newly developed disciplinary matrix. The main finding is that the idea of a duality of body (...) and mind has to be rejected as an incomplete representation of reality: in the universe of the EPT, man hás to be a trinity of body, spirit and soul, where ‘body’, ‘spirit’ and ‘soul’ are names for distinct physical things. The mind is then a mere idea that arises from (self-)experience, but without onto-logical connotation. In addition a mechanism for mental causation, which implies a principle of choice, is rigorously formulated. The main conclusion is that a fundamentally new physicalist approach to the mind-body problem, according to which man has a free will, has been formulated strictly within the language of the EPT. Further physical research is then required to describe how the interaction between body, spirit and soul is to be understood in terms of fundamental interactions. In addition, further philosophical-theological research has to establish whether the presently discussed trinity and the trinities of body, spirit and soul mentioned in the Bible and the Vedic texts are one and the same thing. -/- Edit: a revised version of this preprint has been published as a book chapter. (shrink)

Causal selection is the cognitive process through which one or more elements in a complex causal structure are singled out as actual causes of a certain effect. In this paper, we report on an experiment in which we investigated the role of moral and temporal factors in causal selection. Our results are as follows. First, when presented with a temporal chain in which two human agents perform the same action one after the other, subjects tend to judge the later agent (...) to be the actual cause. Second, the impact of temporal location on causal selection is almost canceled out if the later agent did not violate a norm while the former did. We argue that this is due to the impact that judgments of norm violation have on causal selection—even if the violated norm has nothing to do with the obtaining effect. Third, moral judgments about the effect influence causal selection even in the case in which agents could not have foreseen the effect and did not intend to bring it about. We discuss our findings in connection to recent theories of the role of moral judgment in causal reasoning, on the one hand, and to probabilistic models of temporal location, on the other. (shrink)

I present three reasons why philosophers of science should be more concerned about violations of causal faithfulness (CF). In complex evolved systems, mechanisms for maintaining various equilibrium states are highly likely to violate CF. Even when such systems do not precisely violate CF, they may nevertheless generate precisely the same problems for inferring causal structure from probabilistic relationships in data as do genuine CF-violations. Thus, potential CF-violations are particularly germane to experimental science when we rely on probabilistic information (...) to uncover the DAG, rather than already knowing the DAG from which we could predict the right experiments to ‘catch out’ the hidden causal relationships. (shrink)

State of art paper on the topic causation, around the problem of the nature of causation. Central theories of contemporary philosophical literature are discussed and analysed, namely, regularity theories of Hume and Mackie, counterfactual theories of Lewis, probabilistic theories of Reichenbach, Lewis and Menzies and causal processes theories of Salmon and Dowe.

Comparing different causes’ importance, and apportioning responsibility between them, requires making good sense of the notion of partial explanation, that is, of degree of explanation. How much is this subjective, how much objective? If the causes in question are probabilistic, how much is the outcome due to them and how much to simple chance? I formulate the notion of degree of causation, or effect size, relating it to influential recent work in the literature on causation. I examine (...) to what extent mainstream social science methods--both quantitative and qualitative--succeed in establishing effect sizes so understood. The answer turns out to be, roughly: only to some extent. Next, the standard understanding of effect size, even though widespread, still has several underappreciated consequences. I detail some of those. Finally, I discuss the separate issue of explanandum-dependence, which is essential to assessing any cause’s explanatory importance and yet which has been comparatively neglected. (shrink)

Fred Dretske, Michael Tooley, and David Armstrong accept a theory of governing laws of nature according to which laws are atomic states of affairs that necessitate corresponding natural regularities. Some philosophers object to the Dretske/Tooley/Armstrong theory on the grounds that there is no illuminating account of the necessary connection between governing law and natural regularity. In response, Michael Tooley has provided a reductive account of this necessary connection in his book Causation (1987). In this essay, I discuss an improved (...) version of his account and argue that it fails. First, the account cannot be extended to explain the necessary connection between certain sorts of laws—namely, probabilistic laws and laws relating structural universals—and their corresponding regularities. Second, Tooley’s account succeeds only by (very subtly) incorporating primitive necessity elsewhere, so the problem of avoiding primitive necessity is merely relocated. (shrink)

Medicine, like law, is a pragmatic, probabilistic activity. Both require that decisions be made on the basis of available evidence, within a limited time. In contrast to law, medicine, particularly evidence-based medicine as it is currently practiced, aspires to a scientific standard of proof, one that is more certain than the standards of proof courts apply in civil and criminal proceedings. But medicine, as Dr. William Osler put it, is an "art of probabilities," or at best, a "science of (...) uncertainty." One can better practice medicine by using other evidentiary standards in addition to the "scientific." To employ only the scientific standard of proof is inappropriate, if not impossible; furthermore, as this review will show, its application in medicine is fraught with bias. Evidence is information. It supports or undermines a proposition, whether a hypothesis in science, a diagnosis in medicine, or a fact or point in question in a legal investigation. In medicine, physicians marshal evidence to make decisions on how to best prevent, diagnose, and treat disease, and improve health. In law, courts decide the facts and render justice. Judges and juries assess evidence to establish liability, to settle custody and medical issues, and to determine a defendant's guilt or innocence. (shrink)

There is a middle ground of imperfect knowledge in fields like medicine and the social sciences. It stands between our day-to-day relatively certain knowledge obtained from ordinary basic observation of regularities in our world and our knowledge from well-validated theories in the physical sciences. -/- The latter enable reliable prediction a great deal of the time of the happening of events never before experienced. The former enable prediction only of what has happened before and beyond that of educated guesses which (...) may sometimes prove right and others not when we test them. -/- The imperfection of our knowledge between those limits is a consequence of complexity. -/- Reductionist empiricism fails when faced with complexity but we all still have to live in a complex world where reductionist science cannot help us devise reliable theories from which we can predict the behaviour of the world around us. We cannot predict reliably. We can only monitor actively and manage actively the world around us if we want to try to control our environment. We have a limited prediction horizon. -/- Science and empiricism work well if we want to send Voyager I and II over 3 billion miles away but not for most other things. -/- In medicine how and when to apply probabilistic, conjectural, incomplete medical theories and explanations requires professional expertise, intuition and judgement (non scientific knowledge) which is essential. Medical diagnosis is a skill of predicting from knowledge of what has happened before that the past will recur in the current patient applying expertise and intuition from knowledge and experience of prior cases and probabilistic medical research and theory. The physician is left to an educated guess as to how the future will develop for the particular individual patient the focus of his or her current attention. (shrink)

In many spheres, the law takes the legal concept of causation to correspond to the folk concept (the correspondence assumption). Courts, including the US Supreme Court, tend to insist on the "common understanding" and that which is "natural to say" (Burrage v. United States) when it comes to expressions relating to causation, and frequently refuse to clarify the expression to juries. As recent work in psychology and experimental philosophy has uncovered, lay attributions of causation are susceptible to (...) a great number of unexpected factors, some of which seem rather peripheral to causation. One of those is the norm effect (Knobe & Fraser, 2008): Agents who, in acting as they do, break a salient norm, are more likely to be considered as having caused a certain consequence than when they do not violate a norm. According to some (e.g., Alicke, 1992) this constitutes a bias. According to others (e.g., Sytsma, 2020), the folk concept of causation is sensitive to normative factors, and there’s nothing wrong with that. In this paper, we explore the question whether the norm effect should be considered a bias from the legal perspective on the one hand, and from the psychological perspective on the other. To do this, we test whether norms which are nonpertinent to the consequences or outright silly also impact causation judgements. The data from two preregistered experiments (total N=593) clearly show they do. This, we argue, makes the bias interpretation plausible from the psychological perspective, and both plausible and problematic from the legal perspective. It also shows that the law should abstain from unreflectively assuming conceptual correspondence between legal and ordinary language concepts. (shrink)

How can different individuals' probability assignments to some events be aggregated into a collective probability assignment? Classic results on this problem assume that the set of relevant events -- the agenda -- is a sigma-algebra and is thus closed under disjunction (union) and conjunction (intersection). We drop this demanding assumption and explore probabilistic opinion pooling on general agendas. One might be interested in the probability of rain and that of an interest-rate increase, but not in the probability of rain (...) or an interest-rate increase. We characterize linear pooling and neutral pooling for general agendas, with classic results as special cases for agendas that are sigma-algebras. As an illustrative application, we also consider probabilistic preference aggregation. Finally, we compare our results with existing results on binary judgment aggregation and Arrovian preference aggregation. This paper is the first of two self-contained, but technically related companion papers inspired by binary judgment-aggregation theory. (shrink)

Which country, politician, or policy is more of a cause of the Covid-19 pandemic death toll? Which of the two factories causally contributed more to the pollution of the nearby river? A wide-ranging portion of our everyday thought and talk, and attitudes rely on a graded notion of causation. However, it is sometimes highlighted that on most contemporary accounts, causation is on-off. Some philosophers further question the legitimacy of talk of degrees of causation and suggest that we (...) avoid it. Some hold that the notion of degrees of causation is an illusion. In this paper, I’ll argue that causation does come in degrees. (shrink)

Are probabilism and special relativity compatible? Dieks argues that they are. But the possible universe he specifies, designed to exemplify both probabilism and special relativity, either incorporates a universal “now”, or amounts to a many world universe, or fails to have any one definite overall Minkowskian-type space-time structure. Probabilism and special relativity appear to be incompatible after all. What is at issue is not whether “the flow of time” can be reconciled with special relativity, but rather whether explicitly probabilistic (...) versions of quantum theory should be rejected because of incompatibility with special relativity. (shrink)

The epistemic modal auxiliaries must and might are vehicles for expressing the force with which a proposition follows from some body of evidence or information. Standard approaches model these operators using quantificational modal logic, but probabilistic approaches are becoming increasingly influential. According to a traditional view, must is a maximally strong epistemic operator and might is a bare possibility one. A competing account—popular amongst proponents of a probabilisitic turn—says that, given a body of evidence, must \ entails that \\) (...) is high but non-maximal and might \ that \\) is significantly greater than 0. Drawing on several observations concerning the behavior of must, might and similar epistemic operators in evidential contexts, deductive inferences, downplaying and retractions scenarios, and expressions of epistemic tension, I argue that those two influential accounts have systematic descriptive shortcomings. To better make sense of their complex behavior, I propose instead a broadly Kratzerian account according to which must \ entails that \ = 1\) and might \ that \ > 0\), given a body of evidence and a set of normality assumptions about the world. From this perspective, must and might are vehicles for expressing a common mode of reasoning whereby we draw inferences from specific bits of evidence against a rich set of background assumptions—some of which we represent as defeasible—which capture our general expectations about the world. I will show that the predictions of this Kratzerian account can be substantially refined once it is combined with a specific yet independently motivated ‘grammatical’ approach to the computation of scalar implicatures. Finally, I discuss some implications of these results for more general discussions concerning the empirical and theoretical motivation to adopt a probabilisitic semantic framework. (shrink)

I develop a probabilistic account of coherence, and argue that at least in certain respects it is preferable to (at least some of) the main extant probabilistic accounts of coherence: (i) Igor Douven and Wouter Meijs’s account, (ii) Branden Fitelson’s account, (iii) Erik Olsson’s account, and (iv) Tomoji Shogenji’s account. Further, I relate the account to an important, but little discussed, problem for standard varieties of coherentism, viz., the “Problem of Justified Inconsistent Beliefs.”.

Jeagwon Kim's exclusion argument is a well-known argument against non-reductive physicalism in the contemporary debate on mental causation. In this essay, we will first discuss two versions of the exclusion argument: the simple version and the sophisticated version. Secondly, we will take a conservative strategy to defend the kind of non-reductive physicalism initiated by Donald Davidson: the Token Identity Theory. Namely, we will explain where Kim failed to appropriately understand Davidson's work and argue that the simple version and the (...) sophisticated version of the exclusion argument won't work against the clarified token identity theory at all. Lastly, Since much of Kim's inappropriate interpretation of Davidson's work is rooted in their difference in metaphysics, there is reason to believe the proper understanding and evaluation on contemplation as such on mental causation, non-reductive physicalism, and other related problems can't be achieved without finding an appropriate metaphysical frame. (shrink)

Suppose several individuals (e.g., experts on a panel) each assign probabilities to some events. How can these individual probability assignments be aggregated into a single collective probability assignment? This article reviews several proposed solutions to this problem. We focus on three salient proposals: linear pooling (the weighted or unweighted linear averaging of probabilities), geometric pooling (the weighted or unweighted geometric averaging of probabilities), and multiplicative pooling (where probabilities are multiplied rather than averaged). We present axiomatic characterisations of each class of (...) pooling functions (most of them classic, but one new) and argue that linear pooling can be justified procedurally, but not epistemically, while the other two pooling methods can be justified epistemically. The choice between them, in turn, depends on whether the individuals' probability assignments are based on shared information or on private information. We conclude by mentioning a number of other pooling methods. (shrink)

The Problems of Mental Causation. Functionalism in the philosophy of mind identifies mental states with their dispositional connections with other mental states, perceptions and actions. Many theories of the mind have sailed under the Functionalist flag. But what I take to be essential to Functionalism is that mental states are individuated causally: the reality of mental states depends essentially on their causal efficacy.

Real human agents, even when they are rational by everyday standards, sometimes assign different credences to objectively equivalent statements, such as ‘Orwell is a writer’ and ‘E.A. Blair is a writer’, or credences less than 1 to necessarily true statements, such as not-yet-proven theorems of arithmetic. Anna Mahtani calls this the phenomenon of ‘opacity’. Opaque credences seem probabilistically incoherent, which goes against a key modelling assumption of probability theory. I sketch a modelling strategy for capturing opaque credence assignments without abandoning (...) probabilistic coherence. I draw on ideas from judgement-aggregation theory, where we face similar challenges of defining the ‘objects of judgement’. (shrink)