Results for 'Causal inference engine'

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  1. Minimal Turing Test and Children's Education.Duan Zhang, Xiaoan Wu & Jijun He - 2022 - Journal of Human Cognition 6 (1):47-58.
    Considerable evidence proves that causal learning and causal understanding greatly enhance our ability to manipulate the physical world and are major factors that distinguish humans from other primates. How do we enable unintelligent robots to think causally, answer the questions raised with "why" and even understand the meaning of such questions? The solution is one of the keys to realizing artificial intelligence. Judea Pearl believes that to achieve human-like intelligence, researchers must start by imitating the intelligence of children, (...)
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  2. 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 (...)
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  3. Reconstructing Past Events: A Study of Engineering Failure Investigations.Yafeng Wang - 2020 - Dissertation, Stanford University
    When a major engineering product failed, a failure investigation is often conducted to prevent similar failures in the future. In this dissertation, I propose an account of the epistemology and methodology of engineering failure investigations, based on a close examination of the documentations on five major plane crash investigations conducted by the National Transportation Safety Board (NTSB). -/- The dissertation is divided into three parts. The first part consists of the five case studies arranged in chronological order: the American Airlines (...)
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  4. Causal Inference from Noise.Nevin Climenhaga, Lane DesAutels & Grant Ramsey - 2021 - Noûs 55 (1):152-170.
    "Correlation is not causation" is one of the mantras of the sciences—a cautionary warning especially to fields like epidemiology and pharmacology where the seduction of compelling correlations naturally leads to causal hypotheses. The standard view from the epistemology of causation is that to tell whether one correlated variable is causing the other, one needs to intervene on the system—the best sort of intervention being a trial that is both randomized and controlled. In this paper, we argue that some purely (...)
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  5. Causal inference in biomedical research.Tudor M. Baetu - 2020 - Biology and Philosophy 35 (4):1-19.
    Current debates surrounding the virtues and shortcomings of randomization are symptomatic of a lack of appreciation of the fact that causation can be inferred by two distinct inference methods, each requiring its own, specific experimental design. There is a non-statistical type of inference associated with controlled experiments in basic biomedical research; and a statistical variety associated with randomized controlled trials in clinical research. I argue that the main difference between the two hinges on the satisfaction of the comparability (...)
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  6.  65
    Wright’s path analysis: Causal inference in the early twentieth century.Zili Dong - 2024 - Theoria. An International Journal for Theory, History and Foundations of Science 39 (1):67–88.
    Despite being a milestone in the history of statistical causal inference, Sewall Wright’s 1918 invention of path analysis did not receive much immediate attention from the statistical and scientific community. Through a careful historical analysis, this paper reveals some previously overlooked philosophical issues concerning the history of causal inference. Placing the invention of path analysis in a broader historical and intellectual context, I portray the scientific community’s initial lack of interest in the method as a natural (...)
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  7. Causal Inference as Inference to the Best Explanation.Barry Ward - manuscript
    We argue that a modified version of Mill’s method of agreement can strongly confirm causal generalizations. This mode of causal inference implicates the explanatory virtues of mechanism, analogy, consilience, and simplicity, and we identify it as a species of Inference to the Best Explanation (IBE). Since rational causal inference provides normative guidance, IBE is not a heuristic for Bayesian rationality. We give it an objective Bayesian formalization, one that has no need of principles of (...)
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  8. Causal Inferences in Repetitive Transcranial Magnetic Stimulation Research: Challenges and Perspectives.Justyna Hobot, Michał Klincewicz, Kristian Sandberg & Michał Wierzchoń - 2021 - Frontiers in Human Neuroscience 14:574.
    Transcranial magnetic stimulation is used to make inferences about relationships between brain areas and their functions because, in contrast to neuroimaging tools, it modulates neuronal activity. The central aim of this article is to critically evaluate to what extent it is possible to draw causal inferences from repetitive TMS data. To that end, we describe the logical limitations of inferences based on rTMS experiments. The presented analysis suggests that rTMS alone does not provide the sort of premises that are (...)
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  9. Informational Virtues, Causal Inference, and Inference to the Best Explanation.Barry Ward - manuscript
    Frank Cabrera argues that informational explanatory virtues—specifically, mechanism, precision, and explanatory scope—cannot be confirmational virtues, since hypotheses that possess them must have a lower probability than less virtuous, entailed hypotheses. We argue against Cabrera’s characterization of confirmational virtue and for an alternative on which the informational virtues clearly are confirmational virtues. Our illustration of their confirmational virtuousness appeals to aspects of causal inference, suggesting that causal inference has a role for the explanatory virtues. We briefly explore (...)
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  10. Social cognition as causal inference: implications for common knowledge and autism.Jakob Hohwy & Colin Palmer - 2014 - In Mattia Gallotti & John Michael (eds.), Objects in Mind. Dordrecht: Springer.
    This chapter explores the idea that the need to establish common knowledge is one feature that makes social cognition stand apart in important ways from cognition in general. We develop this idea on the background of the claim that social cognition is nothing but a type of causal inference. We focus on autism as our test-case, and propose that a specific type of problem with common knowledge processing is implicated in challenges to social cognition in autism spectrum disorder (...)
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  11. Variable definition and causal inference.Peter Spirtes - manuscript
    In the last several decades, a confluence of work in the social sciences, philosophy, statistics, and computer science has developed a theory of causal inference using directed graphs. This theory typically rests either explicitly or implicitly on two major assumptions.
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  12. Causal Factors, Causal Inference, Causal Explanation.Elliott Sober & David Papineau - 1986 - Aristotelian Society Supplementary Volume 60 (1):97-136.
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  13. A look at the inference engine underlying ‘evolutionary epistemology’ accounts of the production of heuristics.Philippe Gagnon - 2012 - In Dirk Evers, Antje Jackelén & Michael Fuller (eds.), Is Religion Natural? ESSSAT Yearbook 2011-2012. Forthcoming.
    This paper evaluates the claim that it is possible to use nature’s variation in conjunction with retention and selection on the one hand, and the absence of ultimate groundedness of hypotheses generated by the human mind as it knows on the other hand, to discard the ascription of ultimate certainty to the rationality of human conjectures in the cognitive realm. This leads to an evaluation of the further assumption that successful hypotheses with specific applications, in other words heuristics, seem to (...)
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  14. Recipes for Science: An Introduction to Scientific Methods and Reasoning.Angela Potochnik, Matteo Colombo & Cory Wright - 2018 - New York: Routledge.
    There is widespread recognition at universities that a proper understanding of science is needed for all undergraduates. Good jobs are increasingly found in fields related to Science, Technology, Engineering, and Medicine, and science now enters almost all aspects of our daily lives. For these reasons, scientific literacy and an understanding of scientific methodology are a foundational part of any undergraduate education. Recipes for Science provides an accessible introduction to the main concepts and methods of scientific reasoning. With the help of (...)
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  15. 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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  16. How do medical researchers make causal inferences?Olaf Dammann, Ted Poston & Paul Thagard - 2019 - In Kevin McCain (ed.), What is Scientific Knowledge?: An Introduction to Contemporary Epistemology of Science. New York: Routledge.
    Bradford Hill (1965) highlighted nine aspects of the complex evidential situation a medical researcher faces when determining whether a causal relation exists between a disease and various conditions associated with it. These aspects are widely cited in the literature on epidemiological inference as justifying an inference to a causal claim, but the epistemological basis of the Hill aspects is not understood. We offer an explanatory coherentist interpretation, explicated by Thagard's ECHO model of explanatory coherence. The ECHO (...)
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  17. Mechanizmy predykcyjne i ich normatywność [Predictive mechanisms and their normativity].Michał Piekarski - 2020 - Warszawa, Polska: Liberi Libri.
    The aim of this study is to justify the belief that there are biological normative mechanisms that fulfill non-trivial causal roles in the explanations (as formulated by researchers) of actions and behaviors present in specific systems. One example of such mechanisms is the predictive mechanisms described and explained by predictive processing (hereinafter PP), which (1) guide actions and (2) shape causal transitions between states that have specific content and fulfillment conditions (e.g. mental states). Therefore, I am guided by (...)
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  18. A (naive) view of conspiracy as collective action.M. R. X. Dentith - 2018 - Filosofia E Collettività 22:61-71.
    Conspiracies are, by definition, a group activity; to conspire requires two or more people working together towards some end, typically in secret. Conspirators have intentions; this is borne out by the fact they want some end and are willing to engage in action to achieve. Of course, what these intentions are can be hard to fathom: historians have written a lot about the intentions of the assassins of Julius Caesar, for example; did they want to restore the Republic; was Marcus (...)
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  19. Testing the mechanistic-universe paradigm using chaotic systems.Yehonatan Knoll - manuscript
    We humans are natural-born engineers. As such, we model after machines not only isolated, naturally occurring systems, but also the basic laws of physics, sharing with machines a local-evolution-of-state `grammar'. However, previous work by the author casts doubt upon this mechanistic paradigm, suggesting that it is to blame for the stubbornness of many open problems in physics. Simple experiments are therefore proposed to identify `non-machines'. In one experiment, `non mechanistic correlations' in the spirit of Bell are sought in a pair (...)
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  20. Causal Modeling Semantics for Counterfactuals with Disjunctive Antecedents.Giuliano Rosella & Jan Sprenger - manuscript
    Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic formulas. Our main idea is to assign a probability to a counterfactual (A ∨ B) > C at a causal model M as (...)
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  21. Causation without the causal theory of action.Elena Popa - 2022 - Human Affairs 32 (4):389-393.
    This paper takes a critical stance on Tallis’s separation of causation and agency. While his critique of the causal theory of action and the assumptions about causation underlying different versions of determinism, including the one based on neuroscience is right, his rejection of causation (of all sorts) has implausible consequences. Denying the link between action and causation amounts to overlooking the role action plays in causal inference and in the origin of causal concepts. I suggest that (...)
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  22. Causal Confirmation Measures: From Simpson’s Paradox to COVID-19.Chenguang Lu - 2023 - Entropy 25 (1):143.
    When we compare the influences of two causes on an outcome, if the conclusion from every group is against that from the conflation, we think there is Simpson’s Paradox. The Existing Causal Inference Theory (ECIT) can make the overall conclusion consistent with the grouping conclusion by removing the confounder’s influence to eliminate the paradox. The ECIT uses relative risk difference Pd = max(0, (R − 1)/R) (R denotes the risk ratio) as the probability of causation. In contrast, Philosopher (...)
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  23. Causal Confirmation Measures: From Simpson’s Paradox to COVID-19.Chenguang Lu - 2023 - Entropy 25 (1):143.
    When we compare the influences of two causes on an outcome, if the conclusion from every group is against that from the conflation, we think there is Simpson’s Paradox. The Existing Causal Inference Theory (ECIT) can make the overall conclusion consistent with the grouping conclusion by removing the confounder’s influence to eliminate the paradox. The ECIT uses relative risk difference Pd = max(0, (R − 1)/R) (R denotes the risk ratio) as the probability of causation. In contrast, Philosopher (...)
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  24. Conceptual Engineering Should be Empirical.Ethan Landes - manuscript
    Conceptual engineering is a philosophical method that aims to design and spread conceptual and linguistic devices to cause meaningful changes in the world. So far, however, conceptual engineers have struggled to successfully spread the conceptual and linguistic entities they have designed to their target communities. This paper argues that conceptual engineering is far more likely to succeed if it incorporates empirical data and empirical methods. Because the causal factors influencing successful propagation of linguistic or conceptual devices are as complicated (...)
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  25. Cue competition effects and young children's causal and counterfactual inferences.Teresa McCormack, Stephen Andrew Butterfill, Christoph Hoerl & Patrick Burns - 2009 - Developmental Psychology 45 (6):1563-1575.
    The authors examined cue competition effects in young children using the blicket detector paradigm, in which objects are placed either singly or in pairs on a novel machine and children must judge which objects have the causal power to make the machine work. Cue competition effects were found in a 5- to 6-year-old group but not in a 4-year-old group. Equivalent levels of forward and backward blocking were found in the former group. Children's counterfactual judgments were subsequently examined by (...)
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  26. Inferring causation in epidemiology: mechanisms, black boxes, and contrasts.Alex Broadbent - 2011 - In Phyllis McKay Illari Federica Russo (ed.), Causality in the Sciences. Oxford University Press. pp. 45--69.
    This chapter explores the idea that causal inference is warranted if and only if the mechanism underlying the inferred causal association is identified. This mechanistic stance is discernible in the epidemiological literature, and in the strategies adopted by epidemiologists seeking to establish causal hypotheses. But the exact opposite methodology is also discernible, the black box stance, which asserts that epidemiologists can and should make causal inferences on the basis of their evidence, without worrying about the (...)
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  27. Inference, Explanation, and Asymmetry.Kareem Khalifa, Jared Millson & Mark Risjord - 2018 - Synthese (Suppl 4):929-953.
    Explanation is asymmetric: if A explains B, then B does not explain A. Tradition- ally, the asymmetry of explanation was thought to favor causal accounts of explanation over their rivals, such as those that take explanations to be inferences. In this paper, we develop a new inferential approach to explanation that outperforms causal approaches in accounting for the asymmetry of explanation.
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  28. Are Reasons Causally Relevant for Action? Dharmakīrti and the Embodied Cognition Paradigm.Christian Coseru - 2017 - In Steven Michael Emmanuel (ed.), Buddhist Philosophy: A Comparative Approach. Hoboken: Wiley Blackwell. pp. 109–122.
    How do mental states come to be about something other than their own operations, and thus to serve as ground for effective action? This papers argues that causation in the mental domain should be understood to function on principles of intelligibility (that is, on principles which make it perfectly intelligible for intentions to have a causal role in initiating behavior) rather than on principles of mechanism (that is, on principles which explain how causation works in the physical domain). The (...)
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  29. Is Causal Reasoning Harder Than Probabilistic Reasoning?Milan Mossé, Duligur Ibeling & Thomas Icard - 2024 - Review of Symbolic Logic 17 (1):106-131.
    Many tasks in statistical and causal inference can be construed as problems of entailment in a suitable formal language. We ask whether those problems are more difficult, from a computational perspective, for causal probabilistic languages than for pure probabilistic (or “associational”) languages. Despite several senses in which causal reasoning is indeed more complex—both expressively and inferentially—we show that causal entailment (or satisfiability) problems can be systematically and robustly reduced to purely probabilistic problems. Thus there is (...)
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  30. Causal refutations of idealism.Andrew Chignell - 2010 - Philosophical Quarterly 60 (240):487-507.
    In the ‘Refutation of Idealism’ chapter of the first Critique, Kant argues that the conditions required for having certain kinds of mental episodes are sufficient to guarantee that there are ‘objects in space’ outside us. A perennially influential way of reading this compressed argument is as a kind of causal inference: in order for us to make justified judgements about the order of our inner states, those states must be caused by the successive states of objects in space (...)
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  31. The externalist challenge to conceptual engineering.Steffen Koch - 2021 - Synthese 198 (1):327–348.
    Unlike conceptual analysis, conceptual engineering does not aim to identify the content that our current concepts do have, but the content which these concepts should have. For this method to show the results that its practitioners typically aim for, being able to change meanings seems to be a crucial presupposition. However, certain branches of semantic externalism raise doubts about whether this presupposition can be met. To the extent that meanings are determined by external factors such as causal histories or (...)
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  32. Systems without a graphical causal representation.Daniel M. Hausman, Reuben Stern & Naftali Weinberger - 2014 - Synthese 191 (8):1925-1930.
    There are simple mechanical systems that elude causal representation. We describe one that cannot be represented in a single directed acyclic graph. Our case suggests limitations on the use of causal graphs for causal inference and makes salient the point that causal relations among variables depend upon details of causal setups, including values of variables.
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  33. What is Deductive Inference?Axel Barcelo - manuscript
    What is an inference and when is an inference deductive rather than inductive, abductive, etc. The goal of this paper is precisely to determine what is that we, humans, do when we engage in deduction, i.e., whether there is something that satisfies both our pre-theoretical intuitions and theoretical presuppositions about deduction, as a cognitive process. The paper is structured in two parts: the first one deals with the issue of what is an inference. There, I will defend (...)
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  34. 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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  35. Epistemology as Engineering?Chase B. Wrenn - 2006 - Theoria 72 (1):60-79.
    According to a common objection to epistemological naturalism, no empirical, scientific theory of knowledge can be normative in the way epistemological theories need to be. In response, such naturalists as W.V. Quine have claimed naturalized epistemology can be normative by emulating engineering disciplines and addressing the relations of causal efficacy between our cognitive means and ends. This paper evaluates that "engineering reply" and finds it a mixed success. Based on consideration of what it might mean to call a theory (...)
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  36. How inference isn’t blind: Self-conscious inference and its role in doxastic agency.David Jenkins - 2019 - Dissertation, King’s College London
    This thesis brings together two concerns. The first is the nature of inference—what it is to infer—where inference is understood as a distinctive kind of conscious and self-conscious occurrence. The second concern is the possibility of doxastic agency. To be capable of doxastic agency is to be such that one is capable of directly exercising agency over one’s beliefs. It is to be capable of exercising agency over one’s beliefs in a way which does not amount to mere (...)
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  37. Epistemic Paternalism via Conceptual Engineering.Eve Kitsik - 2023 - Journal of the American Philosophical Association 9 (4):616-635.
    This essay focuses on conceptual engineers who aim to improve other people's patterns of inference and attention by shaping their concepts. Such conceptual engineers sometimes engage in a form of epistemic paternalism that I call paternalistic cognitive engineering: instead of explicitly persuading, informing and educating others, the engineers non-consultatively rely on assumptions about the target agents’ cognitive systems to improve their belief forming. The target agents could reasonably regard such benevolent exercises of control as violating their sovereignty over their (...)
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  38. Causal Scepticism or Invisible Cement.David-Hillel Ruben - 1982 - Ratio (Misc.) 24 (2):161.
    I defend the view, hardly original with me, that there is no evidence, deductive or non-deductive, for any of our causal beliefs, that does not already assume that there are some causal connections, and hence that there is no way in which experience on its own, or with causalität-free principles, can support the structure of out causal knowledge. The deductive case is perhaps obvious. In the case of non-deductive arguments, I consider how experience of constant conjunctions, together (...)
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  39. Evidence and Inductive Inference.Nevin Climenhaga - 2022 - In Maria Lasonen-Aarnio & Clayton Littlejohn (eds.), The Routledge Handbook of the Philosophy of Evidence. New York, NY: Routledge. pp. 435-449.
    This chapter presents a typology of the different kinds of inductive inferences we can draw from our evidence, based on the explanatory relationship between evidence and conclusion. Drawing on the literature on graphical models of explanation, I divide inductive inferences into (a) downwards inferences, which proceed from cause to effect, (b) upwards inferences, which proceed from effect to cause, and (c) sideways inferences, which proceed first from effect to cause and then from that cause to an additional effect. I further (...)
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  40. Inference as a Mental Act.David Hunter - 2009 - In Lucy O'Brien & Matthew Soteriou (eds.), Mental actions. New York: Oxford University Press.
    I will argue that a person is causally responsible for believing what she does. Through inference, she can sustain and change her perspective on the world. When she draws an inference, she causes herself to keep or to change her take on things. In a literal sense, she makes up her own mind as to how things are. And, I will suggest, she can do this voluntarily. It is in part because she is causally responsible for believing what (...)
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  41. Competing Conceptual Inferences and the Limits of Experimental Jurisprudence.Jonathan Lewis - forthcoming - In Kevin P. Tobia (ed.), The Cambridge Handbook of Experimental Jurisprudence. Cambridge University Press.
    Legal concepts can sometimes be unclear, leading to disagreements concerning their contents and inconsistencies in their application. At other times, the legal application of a concept can be entirely clear, sharp, and free of confusions, yet conflict with the ways in which ordinary people or other relevant stakeholders think about the concept. The aim of this chapter is to investigate the role of experimental jurisprudence in articulating and, ultimately, dealing with competing conceptual inferences either within a specific domain (e.g., legal (...)
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  42. Causal Decision Theory and EPR correlations.Arif Ahmed & Adam Caulton - 2014 - Synthese 191 (18):4315-4352.
    The paper argues that on three out of eight possible hypotheses about the EPR experiment we can construct novel and realistic decision problems on which (a) Causal Decision Theory and Evidential Decision Theory conflict (b) Causal Decision Theory and the EPR statistics conflict. We infer that anyone who fully accepts any of these three hypotheses has strong reasons to reject Causal Decision Theory. Finally, we extend the original construction to show that anyone who gives any of the (...)
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  43. 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 (...)
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  44. Causality, computing, and complexity.Russ Abbott - 2015
    I discuss two categories of causal relationships: primitive causal interactions of the sort characterized by Phil Dowe and the more general manipulable causal relationships as defined by James Woodward. All primitive causal interactions are manipulable causal relationships, but there are manipulable causal relationships that are not primitive causal interactions. I’ll call the latter constructed causal relationships, and I’ll argue that constructed causal relationships serve as a foundation for both computing and complex (...)
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  45.  18
    Competing Conceptual Inferences and the Limits of Experimental Jurisprudence.Jonathan Lewis - forthcoming - In Kevin P. Tobia (ed.), The Cambridge Handbook of Experimental Jurisprudence. Cambridge University Press.
    Legal concepts can sometimes be unclear, leading to disagreements concerning their contents and inconsistencies in their application. At other times, the legal application of a concept can be entirely clear, sharp, and free of confusions, yet conflict with the ways in which ordinary people or other relevant stakeholders think about the concept. The aim of this chapter is to investigate the role of experimental jurisprudence in articulating and, ultimately, dealing with competing conceptual inferences either within a specific domain (e.g., legal (...)
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  46. Feature dependence: A method for reconstructing actual causes in engineering failure investigations.Yafeng Wang - 2022 - Studies in History and Philosophy of Science Part A 96:100-111.
    Engineering failure investigations seek to reconstruct the actual causes of major engineering failures. The investigators need to establish the existence of certain past events and the actual causal relationships that these events bear to the failures in question. In this paper, I examine one method for reconstructing the actual causes of failure events, which I call "feature dependence". The basic idea of feature dependence is that some features of an event are informative about the features of its causes; therefore, (...)
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  47. Knowledge Engineering and Intelligence Gathering.Nicolae Sfetcu - manuscript
    A process of intelligence gathering begins when a user enters a query into the system. Several objects can match the result of a query with different degrees of relevance. Most systems estimate a numeric value about how well each object matches the query and classifies objects according to this value. Many researches have focused on practices of intelligence gathering. In knowledge engineering, knowledge gathering consists in fiding it from structured and unstructured sources in a way that must represent knowledge in (...)
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  48. The Statistical Nature of Causation.David Papineau - 2022 - The Monist 105 (2):247-275.
    Causation is a macroscopic phenomenon. The temporal asymmetry displayed by causation must somehow emerge along with other asymmetric macroscopic phenomena like entropy increase and the arrow of radiation. I shall approach this issue by considering ‘causal inference’ techniques that allow causal relations to be inferred from sets of observed correlations. I shall show that these techniques are best explained by a reduction of causation to structures of equations with probabilistically independent exogenous terms. This exogenous probabilistic independence imposes (...)
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  49. The Superstitious Lawyer's Inference.J. Adam Carter & Patrick Bondy - 2019 - In Joseph Adam Carter & Patrick Bondy (eds.), Well Founded Belief: New Essays on the Epistemic Basing Relation. New York: Routledge.
    In Lehrer’s case of the superstitious lawyer, a lawyer possesses conclusive evidence for his client’s innocence, and he appreciates that the evidence is conclusive, but the evidence is causally inert with respect to his belief in his client’s innocence. This case has divided epistemologists ever since Lehrer originally proposed it in his argument against causal analyses of knowledge. Some have taken the claim that the lawyer bases his belief on the evidence as a data point for our theories to (...)
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  50. Models and Inferences in Science.Emiliano Ippoliti, Fabio Sterpetti & Thomas Nickles (eds.) - 2016 - Cham: Springer.
    The book answers long-standing questions on scientific modeling and inference across multiple perspectives and disciplines, including logic, mathematics, physics and medicine. The different chapters cover a variety of issues, such as the role models play in scientific practice; the way science shapes our concept of models; ways of modeling the pursuit of scientific knowledge; the relationship between our concept of models and our concept of science. The book also discusses models and scientific explanations; models in the semantic view of (...)
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