Christopher Tollefsen, relying on the new natural law theory, has suggested that in the Phoenix abortion case, the action might be characterized simply as removing the baby rather than killing the baby. Tollefsen and other proponents of the new natural law theory fail to give proper weight to the observable facts of the world around us, and thereby tend to ignore the importance of observable causes in shaping the character of our intentions and our actions. An appreciation of the role (...) of causes reveals that our intentions cannot so readily land on one description of an action and exclude other descriptions. For the Phoenix case, the description “harming the baby” cannot fall outside the doctors’ intention. National Catholic Bioethics Quarterly 14.2 : 273–293. (shrink)

Causal models have proven extremely useful in offering formal representations of causal relationships between a set of variables. Yet in many situations, there are non-causal relationships among variables. For example, we may want variables LDL, HDL, and TOT that represent the level of low-density lipoprotein cholesterol, the level of lipoprotein high-density lipoprotein cholesterol, and total cholesterol level, with the relation LDL+HDL=TOT. This cannot be done in standard causal models, because we can intervene simultaneously on all three (...) variables. The goal of this paper is to extend standard causal models to allow for constraints on settings of variables. Although the extension is relatively straightforward, to make it useful we have to define a new intervention operation that disconnects a variable from a causal equation. We give examples showing the usefulness of this extension, and provide a sound and complete axiomatization for causal models with constraints. (shrink)

The goal of perception is to infer the most plausible source of sensory stimulation. Unisensory perception of temporal order, however, appears to require no inference, since the order of events can be uniquely determined from the order in which sensory signals arrive. Here we demonstrate a novel perceptual illusion that casts doubt on this intuition: in three studies (N=607) the experienced event timings are determined by causality in real-time. Adult observers viewed a simple three-item sequence ACB, which is typically remembered (...) as ABC (Bechlivanidis & Lagnado, 2016), in line with principles of causality. When asked to indicate the time at which events B and C occurred, points of subjective simultaneity shifted so that the assumed cause B appeared earlier and the assumed effect C later, despite full attention and repeated viewings. This first demonstration of causality reversing perceived temporal order cannot be explained by post-perceptual distortion, lapsed attention, or saccades. (shrink)

This paper provides an analysis of explanatory constraints and their role in scientific explanation. This analysis clarifies main characteristics of explanatory constraints, ways in which they differ from “standard” explanatory factors, and the unique roles they play in scientific explanation. While current philosophical work appreciates two main types of explanatory constraints, this paper suggests a new taxonomy: law-based constraints, mathematical constraints, and causal constraints. This classification helps capture unique features of constraint types, the different roles they play in (...) explanation, and it includes causal constraints, which are often overlooked in this literature. (shrink)

All accounts of causality that presuppose the propagation or transfer or some physical stuff to be an essential part of the causal relation rely for the force of their causal claims on a principle of conservation for that stuff. General Relativity does not permit the rigorous formulation of appropriate conservation principles. Consequently, in so far as General Relativity is considered and fundamental physical theory, such accounts of causality cannot be considered fundamental. The continued use of such accounts of (...) causality ought not be proscribed, but justification is due from those who would use them. (shrink)

In this commentary, I first acknowledge points of common ground with the target article by Bruineberg and colleagues. Then, I consider how certain ambiguities could be resolved by considering spatiotemporal constraints on causality. In particular I show how blanket closure emerges from localized interactions between temporally separable subsystems, and how this points to valuable directions of future research. Finally, I close with a process note discussing the allegorical implications of the authors' creative title.

Existing accounts of mechanistic causation are not suited for understanding causation in biological and neural mechanisms because they do not have the resources to capture the unique causal structure of control heterarchies. In this paper, we provide a new account on which the causal powers of mechanisms are grounded by time-dependent, variable constraints. Constraints can also serve as a key bridge concept between the mechanistic approach to explanation and underappreciated work in theoretical biology that sheds light on how (...) biological systems channel energy to actively respond to the environment in adaptive ways, perform work, and fulfill the requirements to maintain themselves far from equilibrium. We show how the framework applies to several concrete examples of control in simple organisms as well as the nervous system of complex organisms. (shrink)

I evaluate Patrick Todd's critique of the idea accepted by many, including (in contemporary philosophy) Nelson Pike and John Martin Fischer, that there can be non‐causal constraints on human actions (including basic actions). I suggest that Todd's critical reflections, although illuminating, are not persuasive. I defend non‐causal constraints in part by putting forward an interpretation of the intuitive idea of the fixity of the past following Carl Ginet: our freedom is the power to add to the given past.

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

Many approaches to quantum gravity -the theory that should account for quantum and gravitational phenomena under the same theoretical umbrella- seem to point at some form of spacetime emergence, i.e., the fact that spacetime is not a fundamental entity of our physical world. This tenet has sparked many philosophical discussions: from the so-called empirical incoherence problem to different accounts of emergence and mechanisms thereof. In this contribution, I focus on the partial order relation of causal set theory and argue (...) that causation can be characterized as an a-temporal constraint over the kinematic space defined by the theory. The relation constrains the growth of a new element/event with respect to the other elements/events of a given set. Therefrom, the flow of time emerges from the collection of the possible growths of the given set, where each possibility is characterized by a classical probability assigned by the dynamics of the theory. (shrink)

In non-causal explanations, some non-causal facts (such as mathematical, modal or metaphysical) are used to explain some physical facts. However, precisely because these explanations abstract away from causal facts, they face two challenges: 1) it is not clear why would one rather than the other non-causal explanantia be relevant for the explanandum; and 2) why would standing in a particular explanatory relation (e.g., “counterfactual dependence”, “constraint”, “entailment”, “constitution”, “grounding”, and so on), and not in some (...) other, be explanatory. I develop an explanatory relevance account which is based on erotetic constraints and show how it addresses these two challenges. (shrink)

A set of constraints forces trade-offs which prevent us from achieving the best possible definitions of the ‘level’ and ‘unit’ of natural selection. This set consists in decisions concerning conflicting pre-analytic intuitions in problematic cases, the relative roles of various conceptual resources in the definitions, which facts need to be accounted for using the definitions, how the relation between selection and evolution orients the definitions, and the relation between the level and unit concepts. Systematic reconstruction and evaluation of leading analyses (...) along these dimensions favors a new functional analysis over Williams’ consequentialist analysis, Sober’s causal analysis, and Dawkins’ teleological analysis. (shrink)

Information about the structure of a causal system can come in the form of observational data—random samples of the system's autonomous behavior—or interventional data—samples conditioned on the particular values of one or more variables that have been experimentally manipulated. Here we study people's ability to infer causal structure from both observation and intervention, and to choose informative interventions on the basis of observational data. In three causal inference tasks, participants were to some degree capable of distinguishing between (...) competing causal hypotheses on the basis of purely observational data. Performance improved substantially when participants were allowed to observe the effects of interventions that they performed on the systems. We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision‐making and maximizing expected information gain. These analyses suggest that people can make rational causal inferences, subject to psychologically reasonable representational assumptions and computationally reasonable processing constraints. (shrink)

ABSTRACT How can imagination generate knowledge when its contents are voluntarily determined? Several philosophers have recently answered this question by pointing to the constraints that underpin imagination when it plays knowledge-generating roles. Nevertheless, little has been said about the nature of these constraints. In this paper, I argue that the constraints that underpin sensory imagination come from the structure of causal probabilistic generative models, a construct that has been highly influential in recent cognitive science and machine learning. I highlight (...) several attractions of this account, and I favourably contrast it with Peter Langland-Hassan’s account of sensory imagination in terms of the forward models exploited in sensorimotor control. (shrink)

A set of constraints forces trade-offs which prevent us from achieving the best possible definitions of the ‘level’ and ‘unit’ of natural selection. This set consists in decisions concerning conflicting pre-analytic intuitions in problematic cases, the relative roles of various conceptual resources in the definitions, which facts need to be accounted for using the definitions, how the relation between selection and evolution orients the definitions, and the relation between the level and unit concepts. Systematic reconstruction and evaluation of leading analyses (...) along these dimensions favors a new functional analysis over Williams’ consequentialist analysis, Sober’s causal analysis, and Dawkins’ teleological analysis. (shrink)

Causal structure learning algorithms have focused on learning in ”batch-mode”: i.e., when a full dataset is presented. In many domains, however, it is important to learn in an online fashion from sequential or ordered data, whether because of memory storage constraints or because of potential changes in the underlying causal structure over the course of learning. In this paper, we present TDSL, a novel causal structure learning algorithm that processes data sequentially. This algorithm can track changes in (...) the generating causal structure or parameters, and requires significantly less memory in realistic settings. We show by simulation that the algorithm performs comparably to batch-mode learning when the causal structure is stationary, and significantly better in non-stationary environments. (shrink)

Life scientists increasingly rely upon abstraction-based modeling and reasoning strategies for understanding biological phenomena. We introduce the notion of constraint-based reasoning as a fruitful tool for conceptualizing some of these developments. One important role of mathematical abstractions is to impose formal constraints on a search space for possible hypotheses and thereby guide the search for plausible causal models. Formal constraints are, however, not only tools for biological explanations but can be explanatory by virtue of clarifying general dependency-relations and (...) patterning between functions and structures. We describe such situations as constraint-based explanations and argue that these differ from mechanistic strategies in important respects. While mechanistic explanations emphasize change-relating causal features, constraint-based explanations emphasize formal dependencies and generic organizational features that are relatively independent of lower-level changes in causal details. Our distinction between mechanistic and constraint-based explanations is pragmatically motivated by the wish to understand scientific practice. We contend that delineating the affordances and assumptions of different explanatory questions and strategies helps to clarify tensions between diverging scientific practices and the innovative potentials in their combination. Moreover, we show how constraint-based explanation integrates several features shared by otherwise different philosophical accounts of abstract explanatory strategies in biology. (shrink)

Classical dispersion relations are derived from a time-asymmetric constraint. I argue that the standard causal interpretation of this constraint plays a scientifically legitimate role in dispersion theory, and hence provides a counterexample to the causal skepticism advanced by John Norton and others. Norton ([2009]) argues that the causal interpretation of the time-asymmetric constraint is an empty honorific and that the constraint can be motivated by purely non-causal considerations. In this paper I respond (...) to Norton's criticisms and argue that Norton's skepticism derives its force partly by holding causal principles to a standard too high to be met by other scientifically legitimate constraints. (shrink)

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with (...) entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. (shrink)

This paper expounds that besides the well-known spatio-temporal problem there is a causal problem of entanglement: even when one neglects spatio-temporal constraints, the peculiar statistics of EPR/B experiment is inconsistent with usual principles of causal explanation as stated by the theory of causal Bayes nets. The conflict amounts to a dilemma that either there are uncaused correlations or there are caused independences . I argue that the central ideas of causal explanations can be saved if one (...) accepts the latter horn and explains the unfaithful independences by a stable fine-tuning of the causal parameters. (shrink)

We discuss two influential views of unification: mutual information unification (MIU) and common origin unification (COU). We propose a simple probabilistic measure for COU and compare it with Myrvold’s (2003, 2017) probabilistic measure for MIU. We then explore how well these two measures perform in simple causal settings. After highlighting several deficiencies, we propose causal constraints for both measures. A comparison with explanatory power shows that the causal version of COU is one step ahead in simple (...) class='Hi'>causal settings. However, slightly increasing the complexity of the underlying causal structure shows that both measures can easily disagree with explanatory power. The upshot of this is that even sophisticated causally constrained measures for unification ultimately fail to track explanatory relevance. This shows that unification and explanation are not as closely related as many philosophers thought. (shrink)

I propose a new account of homology, according to which homology is a correspondence of developmental mechanisms due to common ancestry, formally defined as an isomorphism of causal graphs over lineages. The semiformal definition highlights the role of homology as a higher-order principle unifying evolutionary models and also provides definite meanings to concepts like constraints, evolvability, and novelty. The novel interpretation of homology suggests a broad perspective that accommodates evolutionary developmental biology and traditional population genetics as distinct but complementary (...) approaches to understand evolution, prompting further empirical and theoretical research. (shrink)

Determinism, causality, chance, free will and divine providence form a class of interlaced problems lying in three domains: philosophy, theology, and physics. Recent article by Dariusz Łukasiewicz in Roczniki Filozoficzne (no. 3, 2020) is a great example. Classical physics, that of Newton and Laplace, may lead to deism: God created the world, but then it goes like a mechanical clock. Quantum mechanics brought some “hope” for a rather naïve theology: God acts in gaps between quanta of indetermination. Obviously, any strict (...) determinism jeopardizes the existence of free will. Yes, but only if human mind follows the laws of physics and only if nothing exists outside the physical limits of space and time. We argue that human action lies in-between two worlds: “earth” and “heavens” using the language of Genesis. In that immaterial world, outside time and space constraints, there is no place for the chain of deterministic events. We discuss, in turn, that the principle of causality, a superior law even in physics, reigns also in the non-material world. Though, determinism in the material universe and causality in both worlds seem to be sufficient conditions, to eliminate “chaotic”, or probabilistic causes from human (and divine) action. (shrink)

Relationalists about episodic memory must endorse a disjunctivist theory of memory-experience according to which cases of genuine memory and cases of total confabulation involve distinct kinds of mental event with different natures. This paper is concerned with a pair of arguments against this view, which are analogues of the ‘causal argument’ and the ‘screening off argument’ that have been pressed in recent literature against relationalist (and hence disjunctivist) theories of perception. The central claim to be advanced is that to (...) deal with these two arguments, memory disjunctivists both can and should draw on resources that are standardly appealed to by rival common factor theories of episodic memory, and, in particular, to the idea that genuine memories and merely apparent ones are to be distinguished, at least in part, in terms of the distinctive ways in which they are caused. On the proposed view, there are substantive causal constraints associated both with cases of genuine memory and with cases of mere confabulation. The resulting theory thus tells us something important about the nature both of genuine memories and of mere confabulations, namely, that such experiences must be caused in certain distinctive ways and cannot occur except as the result of a distinctive sort of causal process. In addition, the theory enables the disjunctivist to offer a unified response to an important pair of arguments against her view. (shrink)

This paper is concerned with reasonings that purport to explain why certain organisms have certain traits by showing that their actual design is better than contrasting designs. Biologists call such reasonings 'functional explanations'. To avoid confusion with other uses of that phrase, I call them 'design explanations'. This paper discusses the structure of design explanations and how they contribute to scientific understanding. Design explanations are contrastive and often compare real organisms to hypothetical organisms that cannot possibly exist. They are not (...) causal but appeal to functional dependencies between an organism's different traits. These explanations point out that because an organism has certain traits, it cannot be alive if the trait to be explained were replaced by a specified alternative. They can be understood from a mechanistic point of view as revealing the constraints on what mechanisms can be alive. (shrink)

In this paper I will examine one way of developing Kant's suggestion that one is free just insofar as he acts according to the dictates of norms or principles. and of his distinction between the Realm of Nature, governed by causes, and the Realm of Freedom, governed by norms and principles. Kant's transcendental machinery—the distinction between Understanding and Reason, the free noumenal self expressed somehow as a causally constrained phenomenal self, and so on—can no longer secure this distinction for us. (...) It is just too mysterious to serve as an explanation of freedom. Yet some distinction between the realm of facts and the realm of norms must be established if the notion of freedom as normative rather than causal constraint is to be redeemed. In this paper I will present a version of this distinction which was not envisioned by Kant, and show how a novel response to the dispute between naturalists and non-naturalists concerning the relation of fact to norm can be developed out of that rendering. I will then argue that the account of human freedom which results from this story needs to be supplemented in just the ways which Hegel claimed Kant's account needed to be supplemented, and will recommend an Hegelian self-expressive successor. (shrink)

We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observed cause, one unobserved common cause, and two unobserved independent causes, depending on the pattern of associations (...) and interventions they saw. We replicated these findings with less‐informative training (Experiments 2 and 3) and a new apparatus (Experiment 3) to show that the pattern of data leads to hidden causal inferences across a range of prior constraints on causal knowledge. (shrink)

Most causal discovery algorithms in the literature exploit an assumption usually referred to as the Causal Faithfulness or Stability Condition. In this paper, we highlight two components of the condition used in constraint-based algorithms, which we call “Adjacency-Faithfulness” and “Orientation- Faithfulness.” We point out that assuming Adjacency-Faithfulness is true, it is possible to test the validity of Orientation- Faithfulness. Motivated by this observation, we explore the consequence of making only the Adjacency-Faithfulness assumption. We show that the familiar (...) PC algorithm has to be modified to be correct under the weaker, Adjacency-Faithfulness assumption. The modified algorithm, called Conservative PC (CPC), checks whether Orientation- Faithfulness holds in the orientation phase, and if not, avoids drawing certain causal conclusions the PC algorithm would draw. Howtion: ever, if the stronger, standard causal Faith-. (shrink)

Radical Embodied Cognition is an anti-representationalist approach to the nature of basic cognition proposed by Daniel Hutto and Erik Myin. While endorsing REC’s arguments against a role for contentful representations in basic cognition we suggest that REC’s ‘teleosemiotic’ approach to intentional targeting results in a ‘grey area’ in which it is not clear what kind of causal-explanatory concept is involved. We propose the concept of constraint-evading surrogacy as a conceptual basis for REC’s account of intentional targeting. The argument (...) is developed via a reconsideration of Peter Godfrey-Smith’s ‘environmental complexity thesis’. We show how constraint-evading surrogacy clarifies REC’s account of intentionality by specifying the role of external natural signs and certain internal states. We argue that the resulting view of intentionality can be extended to proto-biotic autocatalytic systems, offering support for the life-mind continuity thesis. We leave open the question of whether it is helpful to consider constraint-evading surrogacy to be a form of representation, but we suggest that there is a prima facie case for such a view. (shrink)

This paper outlines a theory and computer implementation of causal meanings and reasoning. The meanings depend on possibilities, and there are four weak causal relations: A causes B, A prevents B, A allows B, and A allows not‐B, and two stronger relations of cause and prevention. Thus, A causes B corresponds to three possibilities: A and B, not‐A and B, and not‐A and not‐B, with the temporal constraint that B does not precede A; and the stronger relation (...) conveys only the first and last of these possibilities. Individuals represent these relations in mental models of what is true in the various possibilities. The theory predicts a number of phenomena, and, contrary to many accounts, it implies that the meaning of causation is not probabilistic, differs from the meaning of enabling conditions, and does not depend on causal powers or mechanisms. The theory also implies that causal deductions do not depend on schemas or rules. (shrink)

Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these (...) class='Hi'>causal models. The schema organizes the objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning. (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)

Complexity–nonlinear dynamics for my purposes in this essay–is rich with metaphysical and epistemological implications but is only recently receiving sustained philosophical analysis. I will explore some of the subtleties of causation and constraint in Rayleigh-Bénard convection as an example of a complex phenomenon, and extract some lessons for further philosophical reflection on top-down constraint and causation particularly with respect to causal foundationalism.

Recently, a number of philosophers of science have claimed that much explanation in the sciences, especially in the biomedical and social sciences, is mechanistic explanation. I argue the account of mechanistic explanation provided in this tradition has not been entirely satisfactory, as it has neglected to describe in complete detail the crucial causal structure of mechanistic explanation. I show how the interventionist approach to causation, especially within a structural equations framework, provides a simple and elegant account of the (...) class='Hi'>causal structure of mechanisms. This account explains the many useful insights of traditional accounts of mechanism, such as Carl Craver's account in his book Explaining the Brain , but also helps to correct the omissions of such accounts. One of these omissions is the failure to provide an explicit formulation of a modularity constraint that plays a significant role in mechanistic explanation. One virtue of the interventionist/structural equations framework is that it allows for a simple formulation of a modularity constraint on mechanistic explanation. I illustrate the role of this constraint in the last section of the paper, which describes the form that mechanistic explanation takes in the computational, information-processing paradigm of cognitive psychology. (shrink)

Causal inference plays a central role in behavioral science. Historically, behavioral science methodologies have typically sought to infer a single causal relation. Each of the major approaches to causal inference in the behavioral sciences follows this pattern. Nonetheless, such approaches sometimes differ in the causal relation that they infer. Incremental causal inference offers an alternative to this conceptualization of causal inference that divides the inference into a series of incremental steps. Different steps infer different (...) causal relations. Incremental causal inference is consistent with both causal pluralism and anti-pluralism. However, anti-pluralism places greater constraints the possible topology of sequential inferences. Arguments against causal inference include questioning consistency with causation as an explanatory principle, charging undue complexity, and questioning the need for it. Arguments in favor of incremental inference include better explanation of diverse causal inferences in behavioral science, tailored causal inference, and more detailed and explicit description of causal inference. Incremental causal inference offers a viable and potentially fruitful alternative to approaches limited to a single causal relation. (shrink)

Wood and Spekkens argue that any causal model explaining the EPRB correlations and satisfying the no-signalling constraint must also violate the assumption that the model faithfully reproduces the statistical dependences and independences—a so-called ‘fine-tuning’ of the causal parameters. This includes, in particular, retrocausal explanations of the EPRB correlations. I consider this analysis with a view to enumerating the possible responses an advocate of retrocausal explanations might propose. I focus on the response of Näger, who argues that the (...) central ideas of causal explanations can be saved if one accepts the possibility of a stable fine-tuning of the causal parameters. I argue that in light of this view, a violation of faithfulness does not necessarily rule out retrocausal explanations of the EPRB correlations. However, when we consider a plausible retrocausal picture in some detail, it becomes clear that the causal modelling framework is not a natural arena for representing such an account of retrocausality. _1_ Causal Models, Quantum Mechanics, and Faithfulness _2_ Fine-Tuning _2.1_ Fine-tuning in a retrocausal model _3_ Possible Responses _4_ Quantum Causal Models and Retrocausality _4.1_ A more detailed retrocausal account _4.2_ A model of the EPRB probabilities _4.3_ Mapping to a causal model _5_ Conclusion. (shrink)

This paper surveys the issue of relativistic causality within the framework of algebraic quantum field theory . In doing so, we distinguish various notions of causality formulated in the literature and study their relationships, and thereby we offer what we hope to be a useful taxonomy. We propose that the most direct expression of relativistic causality in AQFT is captured not by the spectrum condition but rather by the axiom of local primitive causality, in that it entails a form of (...) local determinism for quantum fields which generalizes the constraint of no superluminal propagation of classical field theories to relativistic quantum field theory. We discuss the status of the axiom of micro-causality by locating its place within a large family of separability/independence/locality conditions developed for AQFT and also by relating it to so-called no-signalling theorems. And we also provide a critical survey of attempts to understand the implications for relativistic causality of the distant correlations endemic to the states in models of AQFT satisfying the standard axioms, and we provide an assessment of attempts to employ Reichenbach's common cause principle in AQFT to defuse worries that these distant correlations implicate direct causal connections between relatively spacelike events. (shrink)

Visual space can be distinguished from physical space. The first is found in visual experience, while the second is defined independently of perception. Theorists have wondered about the relation between the two. Some investigators have concluded that visual space is non-Euclidean, and that it does not have a single metric structure. Here it is argued that visual space exhibits contraction in all three dimensions with increasing distance from the observer, that experienced features of this contraction are not the same as (...) would be the experience of a perspective projection onto a fronto-parallel plane, and that such contraction is consistent with size constancy. These properties of visual space are different from those that would be predicted if spatial perception resulted from the successful solution of the inverse problem. They are consistent with the notion that optical constraints have been internalized. More generally, they are also consistent with the notion that visual spatial structures bear a resemblance relation to physical spatial structures. This notion supports a type of representational relation that is distinct from mere causal correspondence. The reticence of some philosophers and psychologists to discuss the structure of phenomenal space is diagnosed in terms of the simple materialism and the functionalism of the 1970s and 1980s. (shrink)

This essay discusses a heretofore neglected dimension of one of the most important questions in the realm of political theory: which obstacles that stand in the way of our performing a certain action render us unfree to perform that action? This dimension is concerned with the issue of the causal test that a certain central kind of obstacle—i.e., subjunctive interference—has to pass in order to render us unfree. The aim of this essay is, first, to introduce this issue; and, (...) second, to offer a first analysis of the two possible positions that can be taken with respect to it. (shrink)

Social scientists appeal to various “structures” in their explanations including public policies, economic systems, and social hierarchies. Significant debate surrounds the explanatory relevance of these factors for various outcomes such as health, behavioral, and economic patterns. This paper provides a causal account of social structural explanation that is motivated by Haslanger (2016). This account suggests that social structure can be explanatory in virtue of operating as a causal constraint, which is a causal factor with unique characteristics. (...) A novel causal framework is provided for understanding these explanations–this framework addresses puzzles regarding the mysterious causal influence of social structure, how to understand its relation to individual choice, and what makes it the main explanatory (and causally responsible) factor for various outcomes. (shrink)

In this paper I address the current debate on ontic versus epistemic conceptualizations of mechanistic explanation in the mechanisms literature. Illari recently argued that good explanations are subject to both ontic and epistemic constraints: they must describe mechanisms in the world (ontic aim) in such fashion that they provide understanding of their workings (epistemic aim). Elaborating upon Illari’s ‘integration’ account, I argue that causal role function discovery of mechanisms and their components is an epistemic prerequisite for achieving these two (...) aims. This analysis extends Illari’s account in important ways, putting more pressure on ontic readings of mechanistic explanation and providing an answer to the question how ontic and epistemic constraints on mechanistic explanation are related. I argue these point in terms of cases on memory research drawn from neuroscience and research on extinct neurogenetic mechanisms from early nervous systems biology. (shrink)

Bell inequalities, understood as constraints between classical conditional probabilities, can be derived from a set of assumptions representing a common causal explanation of classical correlations. A similar derivation, however, is not known for Bell inequalities in algebraic quantum field theories establishing constraints for the expectation of specific linear combinations of projections in a quantum state. In the paper we address the question as to whether a ‘common causal justification’ of these non-classical Bell inequalities is possible. We will show (...) that although the classical notion of common causal explanation can readily be generalized for the non-classical case, the Bell inequalities used in quantum theories cannot be derived from these non-classical common causes. Just the opposite is true: for a set of correlations there can be given a non-classical common causal explanation even if they violate the Bell inequalities. This shows that the range of common causal explanations in the non-classical case is wider than that restricted by the Bell inequalities. (shrink)

The principle of insufficient reason assigns equal probabilities to each alternative of a random experiment whenever there is no reason to prefer one over the other. The maximum entropy principle generalizes PIR to the case where statistical information like expectations are given. It is known that both principles result in paradoxical probability updates for joint distributions of cause and effect. This is because constraints on the conditional P P\left result in changes of P P\left that assign higher probability to those (...) values of the cause that offer more options for the effect, suggesting “intentional behavior.” Earlier work therefore suggested sequentially maximizing entropy according to the causal order, but without further justification apart from plausibility on toy examples. We justify causal modifications of PIR and MaxEnt by separating constraints into restrictions for the cause and restrictions for the mechanism that generates the effect from the cause. We further sketch why causal PIR also entails “Information Geometric Causal Inference.” We briefly discuss problems of generalizing the causal version of MaxEnt to arbitrary causal DAGs. (shrink)