Results for 'causal modelling'

945 found
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  1. Causal Models and the Logic of Counterfactuals.Jonathan Vandenburgh - manuscript
    Causal models show promise as a foundation for the semantics of counterfactual sentences. However, current approaches face limitations compared to the alternative similarity theory: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This paper addresses these difficulties using exogenous interventions, where causal interventions change the values of exogenous variables rather than structural equations. This model accommodates judgments about backtracking counterfactuals, extends to logically complex counterfactuals, and validates familiar principles (...)
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  2. Causal Models and Metaphysics - Part 1: Using Causal Models.Jennifer McDonald - forthcoming - Philosophy Compass.
    This paper provides a general introduction to the use of causal models in the metaphysics of causation, specifically structural equation models and directed acyclic graphs. It reviews the formal framework, lays out a method of interpretation capable of representing different underlying metaphysical relations, and describes the use of these models in analyzing causation.
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  3. Should causal models always be Markovian? The case of multi-causal forks in medicine.Donald Gillies & Aidan Sudbury - 2013 - European Journal for Philosophy of Science 3 (3):275-308.
    The development of causal modelling since the 1950s has been accompanied by a number of controversies, the most striking of which concerns the Markov condition. Reichenbach's conjunctive forks did satisfy the Markov condition, while Salmon's interactive forks did not. Subsequently some experts in the field have argued that adequate causal models should always satisfy the Markov condition, while others have claimed that non-Markovian causal models are needed in some cases. This paper argues for the second position (...)
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  4. Causal Models and Metaphysics—Part 2: Interpreting Causal Models.Jennifer McDonald - 2024 - Philosophy Compass 19 (7):e13007.
    This paper addresses the question of what constitutes an apt interpreted model for the purpose of analyzing causation. I first collect universally adopted aptness principles into a basic account, flagging open questions and choice points along the way. I then explore various additional aptness principles that have been proposed in the literature but have not been widely adopted, the motivations behind their proposals, and the concerns with each that stand in the way of universal adoption. I conclude that the remaining (...)
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  5. Building Compressed Causal Models of the World.David Kinney & Tania Lombrozo - forthcoming - Cognitive Psychology.
    A given causal system can be represented in a variety of ways. How do agents determine which variables to include in their causal representations, and at what level of granularity? Using techniques from Bayesian networks, information theory, and decision theory, we develop a formal theory according to which causal representations reflect a trade-off between compression and informativeness, where the optimal trade-off depends on the decision-theoretic value of information for a given agent in a given context. This theory (...)
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  6. Actual Causation: Apt Causal Models and Causal Relativism.Jennifer McDonald - 2022 - Dissertation, The Graduate Center, Cuny
    This dissertation begins by addressing the question of when a causal model is apt for deciding questions of actual causation with respect to some target situation. I first provide relevant background about causal models, explain what makes them promising as a tool for analyzing actual causation, and motivate the need for a theory of aptness as part of such an analysis (Chapter 1). I then define what it is for a model on a given interpretation to be accurate (...)
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  7. Essential Structure for Causal Models.Jennifer McDonald - forthcoming - Australasian Journal of Philosophy.
    This paper introduces and defends a new principle for when a structural equation model is apt for analyzing actual causation. Any such analysis in terms of these models has two components: a recipe for reading claims of actual causation off an apt model, and an articulation of what makes a model apt. The primary focus in the literature has been on the first component. But the problem of structural isomorphs has made the second especially pressing (Hall 2007; Hitchcock 2007a). Those (...)
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  8. (1 other version)Engineering Social Concepts: Feasibility and Causal Models.Eleonore Neufeld - forthcoming - Philosophy and Phenomenological Research.
    How feasible are conceptual engineering projects of social concepts that aim for the engineered concept to be widely adopted in ordinary everyday life? Predominant frameworks on the psychology of concepts that shape work on stereotyping, bias, and machine learning have grim implications for the prospects of conceptual engineers: conceptual engineering efforts are ineffective in promoting certain social-conceptual changes. Specifically, since conceptual components that give rise to problematic social stereotypes are sensitive to statistical structures of the environment, purely conceptual change won’t (...)
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  9. Bread prices and sea levels: why probabilistic causal models need to be monotonic.Vera Hoffmann-Kolss - 2024 - Philosophical Studies:1-16.
    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 (...)
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  10. Broken brakes and dreaming drivers: the heuristic value of causal models in the law.Enno Fischer - 2024 - European Journal for Philosophy of Science 14 (1):1-20.
    Recently, there has been an increased interest in employing model-based definitions of actual causation in legal inquiry. The formal precision of such approaches promises to be an improvement over more traditional approaches. Yet model-based approaches are viable only if suitable models of legal cases can be provided, and providing such models is sometimes difficult. I argue that causal-model-based definitions benefit legal inquiry in an indirect way. They make explicit the causal assumptions that need to be made plausible to (...)
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  11. Diagrammatic Modelling of Causality and Causal Relations.Sabah Al-Fedaghi - manuscript
    It has been stated that the notion of cause and effect is one object of study that sciences and engineering revolve around. Lately, in software engineering, diagrammatic causal inference methods (e.g., Pearl’s model) have gained popularity (e.g., analyzing causes and effects of change in software requirement development). This paper concerns diagrammatical (graphic) models of causal relationships. Specifically, we experiment with using the conceptual language of thinging machines (TMs) as a tool in this context. This would benefit works on (...)
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  12. The causal mechanical model of explanation.James Woodward - 1989 - Minnesota Studies in the Philosophy of Science 13:359-83.
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  13. Causal Complexity and Causal Ontology of Health-Related Quality of Life Model.Tennn Hong-Ui - 2022 - Dissertation, National Yang Ming Chiao Tung University
    Patient-centered care (PCC) is an approach to healthcare that values patients’ preference, need, and autonomy. The estimation of healthcare partly depends on how well PCC is implemented. In addition, the result of clinical research can inform the assessment of the implementation of PCC. In clinical research, health-related quality of life (HRQL) theoretical models offer a conceptual toolbox that informs clinical research and guides the hypotheses generation. Wilson and Cleary (1995) developed the most widely used HRQL theoretical model (Bakas et al., (...)
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  14. Causation and Causal Selection in the Biopsychosocial Model of Health and Disease.Hane Htut Maung - 2021 - European Journal of Analytic Philosophy 17 (2):5-27.
    In The Biopsychosocial Model of Health and Disease, Derek Bolton and Grant Gillett argue that a defensible updated version of the biopsychosocial model requires a metaphysically adequate account of disease causation that can accommodate biological, psychological, and social factors. This present paper offers a philosophical critique of their account of biopsychosocial causation. I argue that their account relies on claims about the normativity and the semantic content of biological information that are metaphysically contentious. Moreover, I suggest that these claims are (...)
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  15. Causal Fictionalism.Antony Eagle - 2024 - In Yafeng Shan (ed.), Alternative Philosophical Approaches to Causation: Beyond Difference-making and Mechanism. Oxford: Oxford University Press.
    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 (...)
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  16. A Model-Invariant Theory of Causation.J. Dmitri Gallow - 2021 - Philosophical Review 130 (1):45-96.
    I provide a theory of causation within the causal modeling framework. In contrast to most of its predecessors, this theory is model-invariant in the following sense: if the theory says that C caused (didn't cause) E in a causal model, M, then it will continue to say that C caused (didn't cause) E once we've removed an inessential variable from M. I suggest that, if this theory is true, then we should understand a cause as something which transmits (...)
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  17. Using stable model semantics (SMODELS) in the causal calculator (CCALC).Semra Dogandag, F. Nur Alpaslan & Varol Akman - 2001 - In Semra Dogandag, F. Nur Alpaslan & Varol Akman (eds.), Proceedings of 10th Turkish Symposium on Artificial Intelligence and Neural Networks (TAINN).
    Action Languages are formal methods of talking about actions and their effects on fluents. One recent approach in planning is to define the domains of the planning problems using action languages. The aim of this research is to find a plan for a system defined in the action language C by translating it into a causal theory and then finding an equivalent logic program. The planning problem will then be reduced to finding the answer set (stable model) of this (...)
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  18. A Model of Causal and Probabilistic Reasoning in Frame Semantics.Vasil Penchev - 2020 - Semantics eJournal (Elsevier: SSRN) 2 (18):1-4.
    Quantum mechanics admits a “linguistic interpretation” if one equates preliminary any quantum state of some whether quantum entity or word, i.e. a wave function interpret-able as an element of the separable complex Hilbert space. All possible Feynman pathways can link to each other any two semantic units such as words or term in any theory. Then, the causal reasoning would correspond to the case of classical mechanics (a single trajectory, in which any next point is causally conditioned), and the (...)
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  19. Causal Modeling and the Efficacy of Action.Holly Andersen - 2019 - In Michael Brent & Lisa Miracchi Titus (eds.), Mental Action and the Conscious Mind. New York, NY: Routledge.
    This paper brings together Thompson's naive action explanation with interventionist modeling of causal structure to show how they work together to produce causal models that go beyond current modeling capabilities, when applied to specifically selected systems. By deploying well-justified assumptions about rationalization, we can strengthen existing causal modeling techniques' inferential power in cases where we take ourselves to be modeling causal systems that also involve actions. The internal connection between means and end exhibited in naive action (...)
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  20. Faithfulness, Coordination and Causal Coincidences.Naftali Weinberger - 2018 - Erkenntnis 83 (2):113-133.
    Within the causal modeling literature, debates about the Causal Faithfulness Condition have concerned whether it is probable that the parameters in causal models will have values such that distinct causal paths will cancel. As the parameters in a model are fixed by the probability distribution over its variables, it is initially puzzling what it means to assign probabilities to these parameters. I propose that to assign a probability to a parameter in a model is to treat (...)
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  21. Causal reasoning.Christoph Hoerl - 2011 - Philosophical Studies 152 (2):167-179.
    The main focus of this paper is the question as to what it is for an individual to think of her environment in terms of a concept of causation, or causal concepts, in contrast to some more primitive ways in which an individual might pick out or register what are in fact causal phenomena. I show how versions of this question arise in the context of two strands of work on causation, represented by Elizabeth Anscombe and Christopher Hitchcock, (...)
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  22. (1 other version)Complements, not competitors: causal and mathematical explanations.Holly Andersen - 2017 - British Journal for the Philosophy of Science 69 (2):485-508.
    A finer-grained delineation of a given explanandum reveals a nexus of closely related causal and non- causal explanations, complementing one another in ways that yield further explanatory traction on the phenomenon in question. By taking a narrower construal of what counts as a causal explanation, a new class of distinctively mathematical explanations pops into focus; Lange’s characterization of distinctively mathematical explanations can be extended to cover these. This new class of distinctively mathematical explanations is illustrated with the (...)
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  23. Normality and actual causal strength.Thomas F. Icard, Jonathan F. Kominsky & Joshua Knobe - 2017 - Cognition 161 (C):80-93.
    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 (...)
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  24. What Can Causal Powers Do for Interventionism? The Problem of Logically Complex Causes.Vera Hoffmann-Kolss - 2023 - In Christopher J. Austin, Anna Marmodoro & Andrea Roselli (eds.), Powers, Parts and Wholes: Essays on the Mereology of Powers. New York, NY: Routledge. pp. 130-141.
    Analyzing causation in terms of Woodward's interventionist theory and describing the structure of the world in terms of causal powers are usually regarded as quite different projects in contemporary philosophy. Interventionists aim to give an account of how causal relations can be empirically discovered and described, without committing themselves to views about what causation really is. Causal powers theorists engage in precisely the latter project, aiming to describe the metaphysical structure of the world. In this paper, I (...)
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  25. The causal structure of natural kinds.Olivier Lemeire - 2021 - Studies in History and Philosophy of Science Part A 85:200-207.
    One primary goal for metaphysical theories of natural kinds is to account for their epistemic fruitfulness. According to cluster theories of natural kinds, this epistemic fruitfulness is grounded in the regular and stable co- occurrence of a broad set of properties. In this paper, I defend the view that such a cluster theory is insufficient to adequately account for the epistemic fruitfulness of kinds. I argue that cluster theories can indeed account for the projectibility of natural kinds, but not for (...)
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  26.  65
    Why the Causal Theory of Reference Fails to Immunize Metaphysical Realism Against Putnam’s Model-Theoretic Arguments.Pietro Lampronti - 2024 - Dissertation, London School of Economics
    In the 1980s, Putnam famously launched a series of model-theoretic attacks on Metaphysical Realism, aimed at establishing a dilemma for the view and ultimately leading to its dismissal. The present work evaluates whether adopting the Causal Theory of Reference saves Realism from Putnam's attacks. As it turns out, the outcome of the analysis revolves around two questions: (a) whether language has an intended reference relation with the world, or in model-theoretic terms, whether theories have an intended model; and (b) (...)
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  27. Causal and Evidential Conditionals.Mario Günther - 2022 - Minds and Machines 32 (4):613-626.
    We put forth an account for when to believe causal and evidential conditionals. The basic idea is to embed a causal model in an agent’s belief state. For the evaluation of conditionals seems to be relative to beliefs about both particular facts and causal relations. Unlike other attempts using causal models, we show that ours can account rather well not only for various causal but also evidential conditionals.
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  28. Causal superseding.Jonathan F. Kominsky, Jonathan Phillips, Tobias Gerstenberg, David Lagnado & Joshua Knobe - 2015 - Cognition 137 (C):196-209.
    When agents violate norms, they are typically judged to be more of a cause of resulting outcomes. In this paper, we suggest that norm violations also affect the causality attributed to other agents, a phenomenon we refer to as "causal superseding." We propose and test a counterfactual reasoning model of this phenomenon in four experiments. Experiments 1 and 2 provide an initial demonstration of the causal superseding effect and distinguish it from previously studied effects. Experiment 3 shows that (...)
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  29. Model robustness as a confirmatory virtue: The case of climate science.Elisabeth A. Lloyd - 2015 - Studies in History and Philosophy of Science Part A 49:58-68.
    I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independentlysupported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I (...)
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  30. Hiddleston’s Causal Modeling Semantics and the Distinction between Forward-Tracking and Backtracking Counterfactuals.Kok Yong Lee - 2017 - Studies in Logic 10 (1):79-94.
    Some cases show that counterfactual conditionals (‘counterfactuals’ for short) are inherently ambiguous, equivocating between forward-tracking and backtracking counterfactu- als. Elsewhere, I have proposed a causal modeling semantics, which takes this phenomenon to be generated by two kinds of causal manipulations. (Lee 2015; Lee 2016) In an important paper (Hiddleston 2005), Eric Hiddleston offers a different causal modeling semantics, which he claims to be able to explain away the inherent ambiguity of counterfactuals. In this paper, I discuss these (...)
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  31. A Psychological Approach to Causal Understanding and the Temporal Asymmetry.Elena Popa - 2020 - Review of Philosophy and Psychology 11 (4):977-994.
    This article provides a conceptual account of causal understanding by connecting current psychological research on time and causality with philosophical debates on the causal asymmetry. I argue that causal relations are viewed as asymmetric because they are understood in temporal terms. I investigate evidence from causal learning and reasoning in both children and adults: causal perception, the temporal priority principle, and the use of temporal cues for causal inference. While this account does not suffice (...)
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  32. Why Attention is Not Explanation: Surgical Intervention and Causal Reasoning about Neural Models.Christopher Grimsley, Elijah Mayfield & Julia Bursten - 2020 - Proceedings of the 12th Conference on Language Resources and Evaluation.
    As the demand for explainable deep learning grows in the evaluation of language technologies, the value of a principled grounding for those explanations grows as well. Here we study the state-of-the-art in explanation for neural models for natural-language processing (NLP) tasks from the viewpoint of philosophy of science. We focus on recent evaluation work that finds brittleness in explanations obtained through attention mechanisms.We harness philosophical accounts of explanation to suggest broader conclusions from these studies. From this analysis, we assert the (...)
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  33. Causal graphs and biological mechanisms.Alexander Gebharter & Marie I. Kaiser - 2014 - In Marie I. Kaiser, Oliver R. Scholz, Daniel Plenge & Andreas Hüttemann (eds.), Explanation in the special science: The case of biology and history. Dordrecht: Springer. pp. 55-86.
    Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research practice (...)
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  34. Causal Set Theory and Growing Block? Not Quite.Marco Forgione - manuscript
    In this contribution, I explore the possibility of characterizing the emergence of time in causal set theory (CST) in terms of the growing block universe (GBU) metaphysics. I show that although GBU seems to be the most intuitive time metaphysics for CST, it leaves us with a number of interpretation problems, independently of which dynamics we choose to favor for the theory —here I shall consider the Classical Sequential Growth and the Covariant model. Discrete general covariance of the CSG (...)
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  35. Causality in medicine with particular reference to the viral causation of cancers.Brendan Clarke - 2011 - Dissertation, University College London
    In this thesis, I give a metascientific account of causality in medicine. I begin with two historical cases of causal discovery. These are the discovery of the causation of Burkitt’s lymphoma by the Epstein-Barr virus, and of the various viral causes suggested for cervical cancer. These historical cases then support a philosophical discussion of causality in medicine. This begins with an introduction to the Russo- Williamson thesis (RWT), and discussion of a range of counter-arguments against it. Despite these, I (...)
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  36. Conditionals and the Hierarchy of Causal Queries.Niels Skovgaard-Olsen, Simon Stephan & Michael R. Waldmann - 2021 - Journal of Experimental Psychology: General 1 (12):2472-2505.
    Recent studies indicate that indicative conditionals like "If people wear masks, the spread of Covid-19 will be diminished" require a probabilistic dependency between their antecedents and consequents to be acceptable (Skovgaard-Olsen et al., 2016). But it is easy to make the slip from this claim to the thesis that indicative conditionals are acceptable only if this probabilistic dependency results from a causal relation between antecedent and consequent. According to Pearl (2009), understanding a causal relation involves multiple, hierarchically organized (...)
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  37. Experimental Philosophy and Causal Attribution.Jonathan Livengood & David Rose - 2016 - In Wesley Buckwalter & Justin Sytsma (eds.), Blackwell Companion to Experimental Philosophy. Malden, MA: Blackwell. pp. 434–449.
    Humans often attribute the things that happen to one or another actual cause. In this chapter, we survey some recent philosophical and psychological research on causal attribution. We pay special attention to the relation between graphical causal modeling and theories of causal attribution. We think that the study of causal attribution is one place where formal and experimental techniques nicely complement one another.
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  38. Reasoning about causality in games.Lewis Hammond, James Fox, Tom Everitt, Ryan Carey, Alessandro Abate & Michael Wooldridge - 2023 - Artificial Intelligence 320 (C):103919.
    Causal reasoning and game-theoretic reasoning are fundamental topics in artificial intelligence, among many other disciplines: this paper is concerned with their intersection. Despite their importance, a formal framework that supports both these forms of reasoning has, until now, been lacking. We offer a solution in the form of (structural) causal games, which can be seen as extending Pearl's causal hierarchy to the game-theoretic domain, or as extending Koller and Milch's multi-agent influence diagrams to the causal domain. (...)
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  39. A critique of the causal theory of memory.Marina Trakas - 2010 - Dissertation, Ecole des Hautes Etudes En Sciences Sociales
    In this Master's dissertation, I try to show that the causal theory of memory, which is the only theory developed so far that at first view seems more plausible and that could be integrated with psychological explanations and investigations of memory, shows some conceptual and ontological problems that go beyond the internal inconsistencies that each version can present. On one hand, the memory phenomenon analyzed is very limited: in general it is reduced to the conscious act of remembering expressed (...)
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  40. Modelling competing legal arguments using Bayesian model comparison and averaging.Martin Neil, Norman Fenton, David Lagnado & Richard David Gill - 2019 - Artificial Intelligence and Law 27 (4):403-430.
    Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and ‘average’ Bayesian models of legal arguments that have been built independently and with no attempt to make them consistent (...)
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  41. Causal feature learning for utility-maximizing agents.David Kinney & David Watson - 2020 - In David Kinney & David Watson (eds.), International Conference on Probabilistic Graphical Models. pp. 257–268.
    Discovering high-level causal relations from low-level data is an important and challenging problem that comes up frequently in the natural and social sciences. In a series of papers, Chalupka etal. (2015, 2016a, 2016b, 2017) develop a procedure forcausal feature learning (CFL) in an effortto automate this task. We argue that CFL does not recommend coarsening in cases where pragmatic considerations rule in favor of it, and recommends coarsening in cases where pragmatic considerations rule against it. We propose a new (...)
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  42. "Because" without "Cause": The Uses and Limits of Non-Causal Explanation.Jonathan Birch - 2008 - Dissertation, University of Cambridge
    In this BA dissertation, I deploy examples of non-causal explanations of physical phenomena as evidence against the view that causal models of explanation can fully account for explanatory practices in science. I begin by discussing the problems faced by Hempel’s models and the causal models built to replace them. I then offer three everyday examples of non-causal explanation, citing sticks, pilots and apples. I suggest a general form for such explanations, under which they can be phrased (...)
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  43. Causality, Human Action and Experimentation: Von Wright's Approach to Causation in Contemporary Perspective.Elena Popa - 2017 - Acta Philosophica Fennica 93:355-373.
    This paper discusses von Wright's theory of causation from Explanation and Understanding and Causality and Determinism in contemporary context. I argue that there are two important common points that von Wright's view shares with the version of manipulability currently supported by Woodward: the analysis of causal relations in a system modelled on controlled experiments, and the explanation of manipulability through counterfactuals - with focus on the counterfactual account of unmanipulable causes. These points also mark von Wright's departure from previous (...)
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  44. Actionability Judgments Cause Knowledge Judgments.John Turri, Wesley Buckwalter & David Rose - 2016 - Thought: A Journal of Philosophy 5 (3):212-222.
    Researchers recently demonstrated a strong direct relationship between judgments about what a person knows and judgments about how a person should act. But it remains unknown whether actionability judgments cause knowledge judgments, or knowledge judgments cause actionability judgments. This paper uses causal modeling to help answer this question. Across two experiments, we found evidence that actionability judgments cause knowledge judgments.
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  45. Norms Affect Prospective Causal Judgments.Paul Henne, Kevin O’Neill, Paul Bello, Sangeet Khemlani & Felipe De Brigard - 2021 - Cognitive Science 45 (1):e12931.
    People more frequently select norm-violating factors, relative to norm- conforming ones, as the cause of some outcome. Until recently, this abnormal-selection effect has been studied using retrospective vignette-based paradigms. We use a novel set of video stimuli to investigate this effect for prospective causal judgments—i.e., judgments about the cause of some future outcome. Four experiments show that people more frequently select norm- violating factors, relative to norm-conforming ones, as the cause of some future outcome. We show that the abnormal-selection (...)
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  46. Which Models of Scientific Explanation Are (In)Compatible with Inference to the Best Explanation?Yunus Prasetya - 2024 - British Journal for the Philosophy of Science 75 (1):209-232.
    In this article, I explore the compatibility of inference to the best explanation (IBE) with several influential models and accounts of scientific explanation. First, I explore the different conceptions of IBE and limit my discussion to two: the heuristic conception and the objective Bayesian conception. Next, I discuss five models of scientific explanation with regard to each model’s compatibility with IBE. I argue that Kitcher’s unificationist account supports IBE; Railton’s deductive–nomological–probabilistic model, Salmon’s statistical-relevance model, and van Fraassen’s erotetic account are (...)
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  47.  99
    Where Does Causal Knowlede in Macroeconomics Come from?Stevan Rakonjac -
    Different methodological approaches to empirical macroeconomics will be described and it will be explained that they represent different answers to the question from the title. Structural approaches require that macroeconometrical research should be explicitly founded on the (micro)economic theory in order to be able to measure the causal structure of the macroeconomic phenomena. Unstructural VAR approach suggest using econometric models to try to find out as much as possible about causal structure from the data, without prior restrictions from (...)
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  48. Anchoring Causal Connections in Physical Concepts.Roland Poellinger & Mario Hubert - 2014 - In M. C. Galavotti (ed.), New Directions in the Philosophy of Science. Cham: Springer. pp. 501-509.
    In their paper "How Fundamental Physics represents Causality", Andreas Bartels and Daniel Wohlfarth maintain that there is place for causality in General Relativity. Their argument contains two steps: First they show that there are time-asymmetric models in General Relativity, then they claim to derive that two events are causally connected if and only if there is a time-asymmetric energy flow from one event to the other. In our comment we first give a short summary of their paper followed by a (...)
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  49. Consciousness and Causal Emergence: Śāntarakṣita Against Physicalism.Christian Coseru - 2014 - In Jonardon Ganeri (ed.), The Oxford Handbook of Indian Philosophy. New York, NY: Oxford University Press. pp. 360–378.
    In challenging the physicalist conception of consciousness advanced by Cārvāka materialists such as Bṛhaspati, the Buddhist philosopher Śāntarakṣita addresses a series of key issues about the nature of causality and the basis of cognition. This chapter considers whether causal accounts of generation for material bodies are adequate in explaining how conscious awareness comes to have the structural features and phenomenal properties that it does. Arguments against reductive physicalism, it is claimed, can benefit from an understanding of the structure of (...)
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  50. When are Purely Predictive Models Best?Robert Northcott - 2017 - Disputatio 9 (47):631-656.
    Can purely predictive models be useful in investigating causal systems? I argue ‘yes’. Moreover, in many cases not only are they useful, they are essential. The alternative is to stick to models or mechanisms drawn from well-understood theory. But a necessary condition for explanation is empirical success, and in many cases in social and field sciences such success can only be achieved by purely predictive models, not by ones drawn from theory. Alas, the attempt to use theory to achieve (...)
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