Results for 'Causal models'

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  1. Causal Models and the Logic of Counterfactuals.Jonathan Vandenburgh -
    Causal models provide a framework for making counterfactual predictions, making them useful for evaluating the truth conditions of counterfactual sentences. However, current causal models for counterfactual semantics face limitations compared to the alternative similarity-based approach: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This paper argues that these limitations arise from the theory of interventions where intervening on variables requires changing structural equations rather than the values (...)
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  2. 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 (...)
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  3. The Causal Mechanical Model of Explanation.James Woodward - 1989 - Minnesota Studies in the Philosophy of Science 13:359-83.
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  4. 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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  5. 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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  6. Causal Superseding.Jonathan F. Kominsky, Jonathan Phillips, Tobias Gerstenberg, David Lagnado & Joshua Knobe - 2015 - Cognition 137:196-209.
    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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  7. Normality and Actual Causal Strength.Thomas Icard, Jonathan Kominsky & Joshua Knobe - 2017 - Cognition 161:80-93.
    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 (...)
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  8. 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 (...)
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  9. Causal Modeling Semantics for Counterfactuals with Disjunctive Antecedents.Giuliano Rosella & Jan Sprenger - manuscript
    This paper applies Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) to the evaluation of the probability of counterfactuals with disjunctive antecedents. Standard CMS is limited to evaluating (the probability of) counterfactuals whose antecedent is a conjunction of atomic formulas. We extend this framework to disjunctive antecedents, and more generally, to any Boolean combinations of atomic formulas. Our main idea is to assign a probability to a counterfactual ( A ∨ B ) > C (...)
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  10. 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 (...)
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  11. 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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  12. Causal Graphs and Biological Mechanisms.Alexander Gebharter & Marie I. Kaiser - 2014 - In Marie I. Kaiser, Oliver Scholz, Daniel Plenge & Andreas Hüttemann (eds.), Explanation in the special sciences: 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 (...)
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  13. 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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  14. 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. (...)
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  15. A Causal Safety Criterion for Knowledge.Jonathan Vandenburgh - manuscript
    Safety purports to explain why cases of accidentally true belief are not knowledge, addressing Gettier cases and cases of belief based on statistical evidence. However, numerous examples suggest that safety fails as a condition on knowledge: a belief can be safe even when one's evidence is clearly insufficient for knowledge and knowledge is compatible with the nearby possibility of error, a situation ruled out by the safety condition. In this paper, I argue for a new modal condition designed to capture (...)
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  16. 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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  17. Causal Modeling and the Efficacy of Action.Holly Andersen - forthcoming - In Michael Brent & Lisa Miracchi Titus (eds.), Mental Action and the Conscious Mind. 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 (...)
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  18. 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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  19. Which Models of Scientific Explanation Are (In)Compatible with IBE?Yunus Prasetya - forthcoming - British Journal for the Philosophy of Science.
    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 Philip Kitcher’s unificationist account supports IBE; Peter Railton’s deductive-nomological-probabilistic model, Wesley Salmon’s statistical-relevance Model, and (...)
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  20. Experimental Philosophy and Causal Attribution.Jonathan Livengood & David Rose - 2016 - In Justin Sytsma & Wesley Buckwalter (eds.), A Companion to Experimental Philosophy. Blackwell.
    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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  21.  40
    Causal Feature Learning for Utility-Maximizing Agents.David Kinney & David Watson - 2020 - In 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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  22.  90
    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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  23. Minimal Models and the Generalized Ontic Conception of Scientific Explanation.Mark Povich - 2018 - British Journal for the Philosophy of Science 69 (1):117-137.
    Batterman and Rice ([2014]) argue that minimal models possess explanatory power that cannot be captured by what they call ‘common features’ approaches to explanation. Minimal models are explanatory, according to Batterman and Rice, not in virtue of accurately representing relevant features, but in virtue of answering three questions that provide a ‘story about why large classes of features are irrelevant to the explanandum phenomenon’ ([2014], p. 356). In this article, I argue, first, that a method (the renormalization group) (...)
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  24. Causally Efficacious Intentions and the Sense of Agency: In Defense of Real Mental Causation.Markus E. Schlosser - 2012 - Journal of Theoretical and Philosophical Psychology 32 (3):135-160.
    Empirical evidence, it has often been argued, undermines our commonsense assumptions concerning the efficacy of conscious intentions. One of the most influential advocates of this challenge has been Daniel Wegner, who has presented an impressive amount of evidence in support of a model of "apparent mental causation". According to Wegner, this model provides the best explanation of numerous curious and pathological cases of behavior. Further, it seems that Benjamin Libet's classic experiment on the initiation of action and the empirical evidence (...)
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  25. 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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  26.  83
    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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  27.  46
    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 (...)
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  28.  35
    Teoria causal da memória: uma introdução em filosofia da memória.Glaupy Fontana Ribas - 2021 - Griot : Revista de Filosofia 21 (3):148-163.
    This paper is an introduction on the Causal Theory of Memory, one of the most discussed theories in philosophy of memory in the present days. We begin with Martin & Deutscher’s formulation of the theory, in which the authors present three criteria in order for a given mental state to be considered an instance of memory, amongst them, the famous causal criterion, which stipulates that a memory must be causally connected to the past experience. Subsequently, we discuss if (...)
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  29. 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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  30. Verisimilitude: A Causal Approach.Robert Northcott - 2013 - Synthese 190 (9):1471-1488.
    I present a new definition of verisimilitude, framed in terms of causes. Roughly speaking, according to it a scientific model is approximately true if it captures accurately the strengths of the causes present in any given situation. Against much of the literature, I argue that any satisfactory account of verisimilitude must inevitably restrict its judgments to context-specific models rather than general theories. We may still endorse—and only need—a relativized notion of scientific progress, understood now not as global advance but (...)
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  31. Exemplar Causality as Similitudo Aequivoca in Peter Auriol.Chiara Paladini - 2018 - In Jacopo Falà & Irene Zavattero (eds.), Divine Ideas in Franciscan Thought (XIIIth-XIVth century). pp. 203-238.
    The aim of this paper is to discuss the theory of exemplary causality of Peter Auriol (1280-1322). Until at least the late 13th century, medieval authors claim that the world is orderly and intelligible because God created it according to the models existing eternally in his mind (i.e. divine ideas). Auriol challenges the view of his predecessors and contemporaries. He argues that assuming divine ideas amounts to assuming multiplicity in God and therefore questioning the principle of his absolute simplicity. (...)
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  32. On the Incompatibility of Dynamical Biological Mechanisms and Causal Graphs.Marcel Weber - 2016 - Philosophy of Science 83 (5):959-971.
    I examine to what extent accounts of mechanisms based on formal interventionist theories of causality can adequately represent biological mechanisms with complex dynamics. Using a differential equation model for a circadian clock mechanism as an example, I first show that there exists an iterative solution that can be interpreted as a structural causal model. Thus, in principle, it is possible to integrate causal difference-making information with dynamical information. However, the differential equation model itself lacks the right modularity properties (...)
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  33. Consciousness and Causal Emergence: Śāntarakṣita Against Physicalism.Christian Coseru - 2017 - In Jonardon Ganeri (ed.), The Oxford Handbook of Indian Philosophy. New York and Oxford: 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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  34. The False Dichotomy Between Causal Realization and Semantic Computation.Marcin Miłkowski - 2017 - Hybris. Internetowy Magazyn Filozoficzny 38:1-21.
    In this paper, I show how semantic factors constrain the understanding of the computational phenomena to be explained so that they help build better mechanistic models. In particular, understanding what cognitive systems may refer to is important in building better models of cognitive processes. For that purpose, a recent study of some phenomena in rats that are capable of ‘entertaining’ future paths (Pfeiffer and Foster 2013) is analyzed. The case shows that the mechanistic account of physical computation may (...)
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  35. Modelling with Words: Narrative and Natural Selection.Dominic K. Dimech - 2017 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 62:20-24.
    I argue that verbal models should be included in a philosophical account of the scientific practice of modelling. Weisberg (2013) has directly opposed this thesis on the grounds that verbal structures, if they are used in science, only merely describe models. I look at examples from Darwin's On the Origin of Species (1859) of verbally constructed narratives that I claim model the general phenomenon of evolution by natural selection. In each of the cases I look at, a particular (...)
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  36. The Good, the Bad, and the Timely: How Temporal Order and Moral Judgment Influence Causal Selection.Kevin Reuter, Lara Kirfel, Raphael van Riel & Luca Barlassina - 2014 - Frontiers in Psychology 5 (1336):1-10.
    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 (...)
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  37. The Proximate–Ultimate Distinction and Evolutionary Developmental Biology: Causal Irrelevance Versus Explanatory Abstraction.Massimo Pigliucci & Raphael Scholl - 2015 - Biology and Philosophy 30 (5):653-670.
    Mayr’s proximate–ultimate distinction has received renewed interest in recent years. Here we discuss its role in arguments about the relevance of developmental to evolutionary biology. We show that two recent critiques of the proximate–ultimate distinction fail to explain why developmental processes in particular should be of interest to evolutionary biologists. We trace these failures to a common problem: both critiques take the proximate–ultimate distinction to neglect specific causal interactions in nature. We argue that this is implausible, and that the (...)
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  38. On the Limits of Causal Modeling: Spatially-Structurally Complex Biological Phenomena.Marie I. Kaiser - 2016 - Philosophy of Science 83 (5):921-933.
    This paper examines the adequacy of causal graph theory as a tool for modeling biological phenomena and formalizing biological explanations. I point out that the causal graph approach reaches it limits when it comes to modeling biological phenomena that involve complex spatial and structural relations. Using a case study from molecular biology, DNA-binding and -recognition of proteins, I argue that causal graph models fail to adequately represent and explain causal phenomena in this field. The inadequacy (...)
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  39. Probabilistic Causality and Multiple Causation.Paul Humphreys - 1980 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1980:25 - 37.
    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 (...)
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  40. Spoils to the Vector - How to Model Causes If You Are a Realist About Powers.Stephen Mumford & Rani Lill Anjum - 2011 - The Monist 94 (1):54-80.
    A standard way of representing causation is with neuron diagrams. This has become popular since the influential work of David Lewis. But it should not be assumed that such representations are metaphysically neutral and amenable to any theory of causation. On the contrary, this way of representing causation already makes several Humean assumptions about what causation is, and which suit Lewis’s programme of Humean Supervenience. An alternative of a vector diagram is better suited for a powers ontology. Causation should be (...)
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  41.  57
    Anchoring Causal Connections in Physical Concepts.Roland Poellinger & Mario Hubert - 2014 - In Maria Clara Galavotti, Dennis Dieks, W. J. Gonzalez, Stephan Hartmann, Thomas Uebel & Marcel Weber (eds.), New Directions in the Philosophy of Science. 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 (...)
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  42.  24
    Mathematical and Non-Causal Explanations: An Introduction.Daniel Kostić - 2019 - Perspectives on Science 1 (27):1-6.
    In the last couple of years, a few seemingly independent debates on scientific explanation have emerged, with several key questions that take different forms in different areas. For example, the questions what makes an explanation distinctly mathematical and are there any non-causal explanations in sciences (i.e., explanations that don’t cite causes in the explanans) sometimes take a form of the question of what makes mathematical models explanatory, especially whether highly idealized models in science can be explanatory and (...)
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  43.  81
    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 (...)
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  44. How to Trace a Causal Process.J. Dmitri Gallow - manuscript
    According to the theory developed here, we may trace out the processes emanating from a cause in such a way that any consequence lying along one of these processes counts as an effect of the cause. This theory gives intuitive verdicts in a diverse range of problem cases from the literature. Its claims about causation will never be retracted when we include additional variables in our model. And it validates some plausible principles about causation, including Sartorio's 'Causes as Difference Makers' (...)
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  45.  40
    Near-Decomposability and the Timescale Relativity of Causal Representations.Naftali Weinberger - 2020 - Philosophy of Science 87 (5):841-856.
    A common strategy for simplifying complex systems involves partitioning them into subsystems whose behaviors are roughly independent of one another at shorter timescales. Dynamic causal models clarify how doing so reveals a system’s nonequilibrium causal relationships. Here I use these models to elucidate the idealizations and abstractions involved in representing a system at a timescale. The models reveal that key features of causal representations—such as which variables are exogenous—may vary with the timescale at which (...)
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  46. A Critique of the Causal Theory of Memory.Marina Trakas - 2010 - Dissertation,
    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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  47.  45
    "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 (...)
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  48. On Structural Accounts of Model-Explanations.Martin King - 2016 - Synthese 193 (9):2761-2778.
    The focus in the literature on scientific explanation has shifted in recent years towards model-based approaches. In recent work, Alisa Bokulich has argued that idealization has a central role to play in explanation. Bokulich claims that certain highly-idealized, structural models can be explanatory, even though they are not considered explanatory by causal, mechanistic, or covering law accounts of explanation. This paper focuses on Bokulich’s account in order to make the more general claim that there are problems with maintaining (...)
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  49.  79
    Alife Models as Epistemic Artefacts.Xabier Barandiaran & Alvaro Moreno - 2006 - In Luis Rocha, Larry Yaeger & Mark Bedau (eds.), Artificial Life X : Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems. MIT Press. pp. 513-519.
    Both the irreducible complexity of biological phenomena and the aim of a universalized biology (life-as-it-could-be) have lead to a deep methodological shift in the study of life; represented by the appearance of ALife, with its claim that computational modelling is the main tool for studying the general principles of biological phenomenology. However this methodological shift implies important questions concerning the aesthetic, engineering and specially the epistemological status of computational models in scientific research: halfway between the well established categories of (...)
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  50. The Case for Regularity in Mechanistic Causal Explanation.Holly Andersen - 2012 - Synthese 189 (3):415-432.
    How regular do mechanisms need to be, in order to count as mechanisms? This paper addresses two arguments for dropping the requirement of regularity from the definition of a mechanism, one motivated by examples from the sciences and the other motivated by metaphysical considerations regarding causation. I defend a broadened regularity requirement on mechanisms that takes the form of a taxonomy of kinds of regularity that mechanisms may exhibit. This taxonomy allows precise explication of the degree and location of regular (...)
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