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  1. Causal Conditionals, Tendency Causal Claims and Statistical Relevance.Michał Sikorski, van Dongen Noah & Jan Sprenger - 2024 - Review of Philosophy and Psychology 1:1-26.
    Indicative conditionals and tendency causal claims are closely related (e.g., Frosch and Byrne, 2012), but despite these connections, they are usually studied separately. A unifying framework could consist in their dependence on probabilistic factors such as high conditional probability and statistical relevance (e.g., Adams, 1975; Eells, 1991; Douven, 2008, 2015). This paper presents a comparative empirical study on differences between judgments on tendency causal claims and indicative conditionals, how these judgments are driven by probabilistic factors, and how these factors differ (...)
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  • Red herrings about relative measures: A response to Hoefer and Krauss.Jacob Stegenga - 2022 - Studies in History and Philosophy of Science Part A 92 (C):56-59.
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  • Three Arguments for Absolute Outcome Measures.Jan Sprenger & Jacob Stegenga - 2017 - Philosophy of Science 84 (5):840-852.
    Data from medical research are typically summarized with various types of outcome measures. We present three arguments in favor of absolute over relative outcome measures. The first argument is from cognitive bias: relative measures promote the reference class fallacy and the overestimation of treatment effectiveness. The second argument is decision-theoretic: absolute measures are superior to relative measures for making a decision between interventions. The third argument is causal: interpreted as measures of causal strength, absolute measures satisfy a set of desirable (...)
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  • Contrastive causal explanation and the explanatoriness of deterministic and probabilistic hypotheses.Elliott Sober - 2020 - European Journal for Philosophy of Science 10 (3):1-15.
    Carl Hempel argued that probabilistic hypotheses are limited in what they can explain. He contended that a hypothesis cannot explain why E is true if the hypothesis says that E has a probability less than 0.5. Wesley Salmon and Richard Jeffrey argued to the contrary, contending that P can explain why E is true even when P says that E’s probability is very low. This debate concerned noncontrastive explananda. Here, a view of contrastive causal explanation is described and defended. It (...)
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  • Mechanistic Explanations and Teleological Functions.Andrew Rubner - forthcoming - British Journal for the Philosophy of Science.
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  • Causal Judgment in the Wild: Evidence from the 2020 U.S. Presidential Election.Tadeg Quillien & Michael Barlev - 2022 - Cognitive Science 46 (2):e13101.
    Cognitive Science, Volume 46, Issue 2, February 2022.
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  • Confidence and gradation in causal judgment.Kevin O'Neill, Paul Henne, Paul Bello, John Pearson & Felipe De Brigard - 2022 - Cognition 223 (C):105036.
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  • Valid for What? On the Very Idea of Unconditional Validity.Cristian Larroulet Philippi - 2021 - Philosophy of the Social Sciences 51 (2):151–175.
    What is a valid measuring instrument? Recent philosophy has attended to logic of justification of measures, such as construct validation, but not to the question of what it means for an instrument to be a valid measure of a construct. A prominent approach grounds validity in the existence of a causal link between the attribute and its detectable manifestations. Some of its proponents claim that, therefore, validity does not depend on pragmatics and research context. In this paper, I cast doubt (...)
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  • ”More of a Cause’: Recent Work on Degrees of Causation and Responsibility.Alex Kaiserman - 2018 - Philosophy Compass 13 (7):e12498.
    It is often natural to compare two events by describing one as ‘more of a cause’ of some effect than the other. But what do such comparisons amount to, exactly? This paper aims to provide a guided tour of the recent literature on ‘degrees of causation’. Section 2 looks at what I call ‘dependence measures’, which arise from thinking of causes as difference‐makers. Section 3 looks at what I call ‘production measures’, which arise from thinking of causes as jointly sufficient (...)
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  • Unification and explanation from a causal perspective.Alexander Gebharter & Christian J. Feldbacher-Escamilla - 2023 - Studies in History and Philosophy of Science Part A 99 (C):28-36.
    We discuss two influential views of unification: mutual information unification (MIU) and common origin unification (COU). We propose a simple probabilistic measure for COU and compare it with Myrvold’s (2003, 2017) probabilistic measure for MIU. We then explore how well these two measures perform in simple causal settings. After highlighting several deficiencies, we propose causal constraints for both measures. A comparison with explanatory power shows that the causal version of COU is one step ahead in simple causal settings. However, slightly (...)
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  • Causation comes in degrees.Huzeyfe Demirtas - 2022 - Synthese 200 (1):1-17.
    Which country, politician, or policy is more of a cause of the Covid-19 pandemic death toll? Which of the two factories causally contributed more to the pollution of the nearby river? A wide-ranging portion of our everyday thought and talk, and attitudes rely on a graded notion of causation. However, it is sometimes highlighted that on most contemporary accounts, causation is on-off. Some philosophers further question the legitimacy of talk of degrees of causation and suggest that we avoid it. Some (...)
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  • Generalized Information Theory Meets Human Cognition: Introducing a Unified Framework to Model Uncertainty and Information Search.Vincenzo Crupi, Jonathan D. Nelson, Björn Meder, Gustavo Cevolani & Katya Tentori - 2018 - Cognitive Science 42 (5):1410-1456.
    Searching for information is critical in many situations. In medicine, for instance, careful choice of a diagnostic test can help narrow down the range of plausible diseases that the patient might have. In a probabilistic framework, test selection is often modeled by assuming that people's goal is to reduce uncertainty about possible states of the world. In cognitive science, psychology, and medical decision making, Shannon entropy is the most prominent and most widely used model to formalize probabilistic uncertainty and the (...)
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  • Responsibility Internalism and Responsibility for AI.Huzeyfe Demirtas - 2023 - Dissertation, Syracuse University
    I argue for responsibility internalism. That is, moral responsibility (i.e., accountability, or being apt for praise or blame) depends only on factors internal to agents. Employing this view, I also argue that no one is responsible for what AI does but this isn’t morally problematic in a way that counts against developing or using AI. Responsibility is grounded in three potential conditions: the control (or freedom) condition, the epistemic (or awareness) condition, and the causal responsibility condition (or consequences). I argue (...)
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