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  1. Unboxing the Concepts in Newcomb’s Paradox: Causation, Prediction, Decision in Causal Knowledge Patterns.Roland Poellinger - manuscript
    In Nozick’s rendition of the decision situation given in Newcomb’s Paradox dominance and the principle of maximum expected utility recommend different strategies. While evidential decision theory seems to be split over which principle to apply and how to interpret the principles in the first place, causal decision theory seems to go for the solution recommended by dominance. As a reply to the CDT proposal by Wolfgang Spohn, who opts for “one-boxing” by employing reflexive decision graphs, I will draw on the (...)
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  2. Reversing the Norm Effect on Causal Attributions.John Schwenkler & Justin Sytsma - manuscript
    Research in the psychology of causal thinking has frequently revealed effects of normative considerations on causal attributions, where participants tend to assign causality more strongly to agents who violate a norm in bringing about an outcome. Across several experiments, we show that it is possible to reverse this norm effect when the outcome in question is good rather than bad: in these cases, participants assign causality more strongly to a norm-conforming agent than to an agent who violates a norm. We (...)
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  3. Paradoxes of Causal Loops in Spacetime.Nicolae Sfetcu - manuscript
    There is, among some scientists and philosophers, the idea that any theory that would allow the time travel would introduce causal issues. These types of temporal paradoxes can be avoided by the Novikov self-consistency principle or by a variation in the interpretation of many worlds with interacting worlds. The world in which we live has, according to David Lewis, a Parmenidean ontology: "a manifold of events in four dimensions," and the occupants of the world are the 4-dimensional aggregates of the (...)
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  4. The Logic of Counterfactuals in Causal Inference.Judea Pearl - manuscript
  5. Often Trusted But Never (Properly) Tested: Evaluating Qualitative Comparative Analysis,.Michael Baumgartner & Alrik Thiem - forthcoming - Sociological Methods & Research.
    To date, hundreds of researchers have employed the method of Qualitative Comparative Analysis (QCA) for the purpose of causal inference. In a recent series of simulation studies, however, several authors have questioned the correctness of QCA in this connection. Some prominent representatives of the method have replied in turn that simulations with artificial data are unsuited for assessing QCA. We take issue with either position in this impasse. On the one hand, we argue that data-driven evaluations of the correctness of (...)
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  6. The C-Word, the P-Word, and Realism in Epidemiology.Alex Broadbent - forthcoming - Synthese:1-16.
    This paper considers an important recent contribution by Miguel Hernán to the ongoing debate about causal inference in epidemiology. Hernán rejects the idea that there is an in-principle epistemic distinction between the results of randomized controlled trials and observational studies: both produce associations which we may be more or less confident interpreting as causal. However, Hernán maintains that trials have a semantic advantage. Observational studies that seek to estimate causal effect risk issuing meaningless statements instead. The POA proposes a solution (...)
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  7. The Fate of Explanatory Reasoning in the Age of Big Data.Frank Cabrera - forthcoming - Philosophy and Technology:1-21.
    In this paper, I critically evaluate several related, provocative claims made by proponents of data-intensive science and “Big Data” which bear on scientific methodology, especially the claim that scientists will soon no longer have any use for familiar concepts like causation and explanation. After introducing the issue, in section 2, I elaborate on the alleged changes to scientific method that feature prominently in discussions of Big Data. In section 3, I argue that these methodological claims are in tension with a (...)
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  8. Causal Reasoning and Meno’s Paradox.Melvin Chen & Lock Yue Chew - forthcoming - AI and Society:1-9.
    Causal reasoning is an aspect of learning, reasoning, and decision-making that involves the cognitive ability to discover relationships between causal relata, learn and understand these causal relationships, and make use of this causal knowledge in prediction, explanation, decision-making, and reasoning in terms of counterfactuals. Can we fully automate causal reasoning? One might feel inclined, on the basis of certain groundbreaking advances in causal epistemology, to reply in the affirmative. The aim of this paper is to demonstrate that one still has (...)
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  9. Process Tracing: Defining the Undefinable.Christopher Clarke - forthcoming - In The Oxford Handbook of Philosophy of Political Science.
    A good definition of process tracing should highlight what is distinctive about process tracing as a methodology of causal inference. I look at eight criteria that are used to define process tracing in the methodological literature, and I dismiss all eight criteria as unhelpful (some because they are too restrictive, and others because they are vacuous). In place of these criteria, I propose four alternative criteria, and I draw a distinction between process tracing for the ultimate aim of testing a (...)
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  10. How Do Medical Researchers Make Causal Inferences?Olaf Dammann, Ted Poston & Paul Thagard - forthcoming - In What is scientific knowledge? An introduction to contemporary epistemology of science. London, UK:
    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 model captures the complexity (...)
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  11. What Caused the Bhopal Gas Tragedy? The Philosophical Importance of Causal and Pragmatic Details.Brian J. Hanley - forthcoming - Philosophy of Science.
    In cases where many causes together bring about an effect, it is common to select some as particularly important. Philosophers since Mill have been pessimistic about analyzing this reasoning due its variability and the multifarious causal and pragmatic details of how it works. I argue Mill was right to think these details matter, but wrong that they preclude philosophical analysis of causal selection. I show that analyzing the pragmatic details of scientific debates about the important causes of the Bhopal Gas (...)
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  12. Social Cognition as Causal Inference: Implications for Common Knowledge and Autism.Jakob Hohwy & Colin Palmer - forthcoming - In John Michael & Mattia Gallotti (eds.), Social Objects and Social Cognition. 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 (ASD). This (...)
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  13. Reflecting on What Philosophy of Epidemiology is and Does, as the Field Comes Into its Own: Introduction to the Special Issue on Philosophy of Epidemiology.Jonathan Michael Kaplan & Sean A. Valles - forthcoming - Synthese:1-10.
    This article is an introduction to the Synthese Special Issue, Philosophy of Epidemiology. The overall goals of the issue are to revisit the state of philosophy of epidemiology and to provide a forum for new voices, approaches, and perspectives in the philosophy of epidemiology literature. The introduction begins by drawing on Geoffrey Rose’s work on how to conceptualize and design interventions for populations, rather than individuals. It then goes on to highlight some themes that emerged in the articles that make (...)
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  14. The Developmental Profile of Temporal Binding: From Childhood to Adulthood.Sara Lorimer, Teresa McCormack, Emma Blakey, David A. Lagnado, Christoph Hoerl, Emma Tecwyn & Marc J. Buehner - forthcoming - Quarterly Journal of Experimental Psychology.
    Temporal binding refers to a phenomenon whereby the time interval between a cause and its effect is perceived as shorter than the same interval separating two unrelated events. We examined the developmental profile of this phenomenon by comparing the performance of groups of children (aged 6-7-, 7-8-, and 9-10- years) and adults on a novel interval estimation task. In Experiment 1, participants made judgments about the time interval between i) their button press and a rocket launch, and ii) a non-causal (...)
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  15. Highlighting the Causal Meaning of Causal Test Questions in Contexts of Norm Violations.Jana Samland & Michael Waldmann - forthcoming - Proceedings of the Cognitive Science Society.
    Experiments have shown that prescriptive norms often influence causal inferences. The reason for this effect is still not clear. One problem of the studies is that the term ‘cause’ in the test questions is ambiguous and can refer to both the causal mechanism and the agent’s accountability. Possibly subjects interpreted the causal test question as a request to assess accountability rather than causality. Scenarios that put more stress on the causal mechanism should therefore yield no norm effect. Consequently, Experiment 1 (...)
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  16. The Epistemology of Causal Selection: Insights From Systems Biology.Beckett Sterner - forthcoming - In C. Kenneth Waters (ed.), Causal Reasoning in Biology. University of Minnesota Press.
    Among the many causes of an event, how do we distinguish the important ones? Are there ways to distinguish among causes on principled grounds that integrate both practical aims and objective knowledge? Psychologist Tania Lombrozo has suggested that causal explanations “identify factors that are ‘exportable’ in the sense that they are likely to subserve future prediction and intervention” (Lombrozo 2010, 327). Hence portable causes are more important precisely because they provide objective information to prediction and intervention as practical aims. However, (...)
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  17. Causation, Responsibility, and Typicality.Justin Sytsma - forthcoming - Review of Philosophy and Psychology:1-21.
    There is ample evidence that violations of injunctive norms impact ordinary causal attributions. This has struck some as deeply surprising, taking the ordinary concept of causation to be purely descriptive. Our explanation of the findings—the responsibility view—rejects this: we contend that the concept is in fact partly normative, being akin to concepts like responsibility and accountability. Based on this account, we predicted a very different pattern of results for causal attributions when an agent violates a statistical norm. And this pattern (...)
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  18. Philosophical Perspectives on Causal Reasoning in Biology. Minnesota Studies in Philosophy of Science. Vol. XXI.Waters C. Kenneth & Woodward James (eds.) - forthcoming - University of Minnesota Press.
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  19. Philosophical Perspectives on Causal Reasoning in Biology.C. Kenneth Waters & James Woodward (eds.) - forthcoming - University of Minnesota Press.
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  20. On Mary Shepherd's Essay Upon the Relation of Cause and Effect.Jessica Wilson - forthcoming - In Eric Schliesser (ed.), Neglected Classics of Philosophy, II. Oxford University Press.
    Mary Shepherd (1777–1847) was a fierce and brilliant critic of Berkeley and Hume, who moreover offered strikingly original positive views about the nature of reality and our access to it which deserve much more attention (and credit, since she anticipates many prominent views) than they have received thus far. By way of illustration, I focus on Shepherd's 1824 Essay Upon the Relation of Cause and Effect, Controverting the Doctrine of Mr. Hume, Concerning the Nature of that Relation (ERCE). After a (...)
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  21. Subjective causal networks and indeterminate suppositional credences.Jiji Zhang, Teddy Seidenfeld & Hailin Liu - forthcoming - Synthese:1-27.
    This paper has two main parts. In the first part, we motivate a kind of indeterminate, suppositional credences by discussing the prospect for a subjective interpretation of a causal Bayesian network, an important tool for causal reasoning in artificial intelligence. A CBN consists of a causal graph and a collection of interventional probabilities. The subjective interpretation in question would take the causal graph in a CBN to represent the causal structure that is believed by an agent, and interventional probabilities in (...)
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  22. Evidence and Inductive Inference.Nevin Climenhaga - 2021 - In Maria Lasonen-Aarnio & Clayton Littlejohn (eds.), The Routledge Handbook of the Philosophy of Evidence. Routledge.
    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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  23. 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 correlational (...)
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  24. 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 effects (...)
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  25. Much Ado About Nothing: The Mental Representation of Omissive Relations.Sangeet Khemlani, Paul Bello, Gordon Briggs, Hillary Harner & Christina Wasylyshyn - 2021 - Frontiers in Psychology 11.
    When the absence of an event causes some outcome, it is an instance of omissive causation. For instance, not eating lunch may cause you to be hungry. Recent psychological proposals concur that the mind represents causal relations, including omissive causal relations, through mental simulation, but they disagree on the form of that simulation. One theory states that people represent omissive causes as force vectors; another states that omissions are representations of contrasting counterfactual simulations; a third argues that people think about (...)
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  26. 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 requirement, which (...)
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  27. Evidence and Explanation in Cicero's On Divination.Frank Cabrera - 2020 - Studies in History and Philosophy of Science Part A 82:34-43.
    In this paper, I examine Cicero’s oft-neglected De Divinatione, a dialogue investigating the legitimacy of the practice of divination. First, I offer a novel analysis of the main arguments for divination given by Quintus, highlighting the fact that he employs two logically distinct argument forms. Next, I turn to the first of the main arguments against divination given by Marcus. Here I show, with the help of modern probabilistic tools, that Marcus’ skeptical response is far from the decisive, proto-naturalistic assault (...)
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  28. Causation in Psychology.John Campbell - 2020 - Harvard University Press.
  29. Averaging Causal Estimators in High Dimensions.Matthew Cefalu & Joseph Antonelli - 2020 - Journal of Causal Inference 8 (1):92-107.
    There has been increasing interest in recent years in the development of approaches to estimate causal effects when the number of potential confounders is prohibitively large. This growth in interest has led to a number of potential estimators one could use in this setting. Each of these estimators has different operating characteristics, and it is unlikely that one estimator will outperform all others across all possible scenarios. Coupling this with the fact that an analyst can never know which approach is (...)
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  30. A Process Model of Causal Reasoning.Zachary J. Davis & Bob Rehder - 2020 - Cognitive Science 44 (5).
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  31. The Explanatory Indispensability of Memory Traces.Felipe De Brigard - 2020 - The Harvard Review of Philosophy 27:23-47.
    During the first half of the twentieth century, many philosophers of memory opposed the postulation of memory traces based on the claim that a satisfactory account of remembering need not include references to causal processes involved in recollection. However, in 1966, an influential paper by Martin and Deutscher showed that causal claims are indeed necessary for a proper account of remembering. This, however, did not settle the issue, as in 1977 Malcolm argued that even if one were to buy Martin (...)
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  32. Causal Discovery and the Problem of Psychological Interventions.Markus I. Eronen - 2020 - New Ideas in Psychology 59:100785.
    Finding causes is a central goal in psychological research. In this paper, I argue based on the interventionist approach to causal discovery that the search for psychological causes faces great obstacles. Psychological interventions are likely to be fat-handed: they change several variables simultaneously, and it is not known to what extent such interventions give leverage for causal inference. Moreover, due to problems of measurement, the degree to which an intervention was fat-handed, or more generally, what the intervention in fact did, (...)
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  33. A Combinatorial Solution to Causal Compatibility.Thomas C. Fraser - 2020 - Journal of Causal Inference 8 (1):22-53.
    Within the field of causal inference, it is desirable to learn the structure of causal relationships holding between a system of variables from the correlations that these variables exhibit; a sub-problem of which is to certify whether or not a given causal hypothesis is compatible with the observed correlations. A particularly challenging setting for assessing causal compatibility is in the presence of partial information; i.e. when some of the variables are hidden/latent. This paper introduces the possible worlds framework as a (...)
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  34. Technology Led to More Abstract Causal Reasoning.Peter Gärdenfors & Marlize Lombard - 2020 - Biology and Philosophy 35 (4):1-23.
    Many animal species use tools, but human technical engagement is more complex. We argue that there is coevolution between technical engagement and advanced forms of causal cognition in the human lineage. As an analytic tool, we present a classification of different forms of causal thinking. Human causal thinking has become detached from space and time, so that instead of just reacting to perceptual input, our minds can simulate actions and forces and their causal consequences. Our main thesis is that, unlike (...)
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  35. 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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  36. Temporal Binding, Causation and Agency: Developing a New Theoretical Framework.Christoph Hoerl, Sara Lorimer, Teresa McCormack, David A. Lagnado, Emma Blakey, Emma C. Tecwyn & Marc J. Buehner - 2020 - Cognitive Science 44 (e12843):1-27.
    In temporal binding, the temporal interval between one event and another, occurring some time later, is subjectively compressed. We discuss two ways in which temporal binding has been conceptualized. In studies showing temporal binding between a voluntary action and its causal consequences, such binding is typically interpreted as providing a measure of an implicit or pre-reflective “sense of agency”. However, temporal binding has also been observed in contexts not involving voluntary action, but only the passive observation of a cause-effect sequence. (...)
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  37. Post-Randomization Biomarker Effect Modification Analysis in an HIV Vaccine Clinical Trial.Michael G. Hudgens, Bryan E. Shepherd, Bryan S. Blette & Peter B. Gilbert - 2020 - Journal of Causal Inference 8 (1):54-69.
    While the HVTN 505 trial showed no overall efficacy of the tested vaccine to prevent HIV infection over placebo, markers measuring immune response to vaccination were strongly correlated with infection. This finding generated the hypothesis that some marker-defined vaccinated subgroups were partially protected whereas others had their risk increased. This hypothesis can be assessed using the principal stratification framework for studying treatment effect modification by an intermediate response variable, using methods in the sub-field of principal surrogate analysis that studies multiple (...)
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  38. What Was Perrin Really Doing in His Proof of the Reality of Atoms?Robert Hudson - 2020 - Hopos: The Journal of the International Society for the History of Philosophy of Science 10 (1):194-218.
  39. 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 technique, (...)
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  40. A Review of Proposed Principles of Causal Non-Monotonic Reasoning. [REVIEW]Patrick Marchisella - 2020 - Australasian Journal of Logic 17 (3):14.
    Within Non-monotonic Reasoning, numerous principles of causal reasoning have been proposed. Many of these principles have been viewed as desirable in formalisms that reason with causality, and have been widely adopted throughout the literature. We provide a critique of these principles, evaluate their suitability for characterising and formulating causal non-monotonic reasoning, and find that most are unsuitable. Further, we discuss a new approach to causal non-monotonic reasoning motivated by how humans typically reason with causality.
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  41. Unifying Gaussian LWF and AMP Chain Graphs to Model Interference.Jose M. Peña - 2020 - Journal of Causal Inference 8 (1):1-21.
    An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs to create a new class of causal models that can represent both interference and non-interference relationships for Gaussian distributions. Specifically, we define the new class of models, introduce global and local and pairwise Markov properties for them, and prove their equivalence. We also (...)
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  42. 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 probabilistic (...)
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  43. COVID-19 and Control: An Essay From a Pragmatic Perspective on Science.Tuomas K. Pernu - 2020 - Multidisciplinary Perspectives on the COVID-19 Pandemic.
    The COVID-19 pandemic has demonstrated how different (even conflicting) interventions on nature can be scientifically justified: interventions can be deemed "effective" only in relation to specific target variables - in relation to variables the values of which we seek to control. Choosing the "right" target variables, in turn, depends on our values and pragmatic aims. This essay is based on a presentation given at the symposium "Multidisciplinary Perspectives on the COVID-19 Pandemic", organised at the Helsinki Collegium for Advanced Studies on (...)
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  44. 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 for correct inferences of causal structure, (...)
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  45. Estimating Population Average Treatment Effects From Experiments with Noncompliance.Jason V. Poulos & Kellie N. Ottoboni - 2020 - Journal of Causal Inference 8 (1):108-130.
    Randomized control trials are the gold standard for estimating causal effects, but often use samples that are non-representative of the actual population of interest. We propose a reweighting method for estimating population average treatment effects in settings with noncompliance. Simulations show the proposed compliance-adjusted population estimator outperforms its unadjusted counterpart when compliance is relatively low and can be predicted by observed covariates. We apply the method to evaluate the effect of Medicaid coverage on health care use for a target population (...)
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  46. How Many Have Died?S. Andrew Schroeder - 2020 - Issues in Science and Technology.
    I look at the two main approaches used to count COVID-19 deaths and show how each of those approaches can appear to both overcount COVID deaths (including deaths it should exclude) and undercount COVID deaths (excluding deaths it should include). I trace this to the fact - well-known to philosophers - that causal attribution is interest-relative. Which deaths we should attribute to COVID (as opposed to other causes) will depend on our particular interests and values. Contrary to what many journalists (...)
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  47. Time and Singular Causation—A Computational Model.Simon Stephan, Ralf Mayrhofer & Michael R. Waldmann - 2020 - Cognitive Science 44 (7).
    Causal queries about singular cases, which inquire whether specific events were causally connected, are prevalent in daily life and important in professional disciplines such as the law, medicine, or engineering. Because causal links cannot be directly observed, singular causation judgments require an assessment of whether a co‐occurrence of two events c and e was causal or simply coincidental. How can this decision be made? Building on previous work by Cheng and Novick (2005) and Stephan and Waldmann (2018), we propose a (...)
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  48. Causality Influences Children's and Adults' Experience of Temporal Order.Emma C. Tecwyn, Christos Bechlivanidis, David A. Lagnado, Christoph Hoerl, Sara Lorimer, Emma Blakey, Teresa McCormack & Marc J. Buehner - 2020 - Developmental Psychology 56 (4):739-755.
    Although it has long been known that time is a cue to causation, recent work with adults has demonstrated that causality can also influence the experience of time. In causal reordering (Bechlivanidis & Lagnado, 2013, 2016) adults tend to report the causally consistent order of events, rather than the correct temporal order. However, the effect has yet to be demonstrated in children. Across four pre-registered experiments, 4- to 10-year-old children (N=813) and adults (N=178) watched a 3-object Michotte-style ‘pseudocollision’. While in (...)
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  49. The Inflation Technique Completely Solves the Causal Compatibility Problem.Elie Wolfe & Miguel Navascués - 2020 - Journal of Causal Inference 8 (1):70-91.
    The causal compatibility question asks whether a given causal structure graph — possibly involving latent variables — constitutes a genuinely plausible causal explanation for a given probability distribution over the graph’s observed categorical variables. Algorithms predicated on merely necessary constraints for causal compatibility typically suffer from false negatives, i.e. they admit incompatible distributions as apparently compatible with the given graph. In 10.1515/jci-2017-0020, one of us introduced the inflation technique for formulating useful relaxations of the causal compatibility problem in terms of (...)
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  50. Unmixing for Causal Inference: Thoughts on McCaffrey and Danks.Kun Zhang & Madelyn R. K. Glymour - 2020 - British Journal for the Philosophy of Science 71 (4):1319-1330.
    McCaffrey and Danks have posed the challenge of discovering causal relations in data drawn from a mixture of distributions as an impossibility result in functional magnetic resonance. We give an algorithm that addresses this problem for the distributions commonly assumed in fMRI studies and find that in testing, it can accurately separate data from mixed distributions. As with other obstacles to automated search, the problem of mixed distributions is not an impossible one, but rather a challenge. 1Introduction2Background3Addressing the Problem4Discussion.
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