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  1. Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Tijdschrift Voor Filosofie 64 (1):201-202.
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  • Normality and actual causal strength.Thomas F. Icard, Jonathan F. Kominsky & Joshua Knobe - 2017 - Cognition 161 (C):80-93.
    Existing research suggests that people's judgments of actual causation can be influenced by the degree to which they regard certain events as normal. We develop an explanation for this phenomenon that draws on standard tools from the literature on graphical causal models and, in particular, on the idea of probabilistic sampling. Using these tools, we propose a new measure of actual causal strength. This measure accurately captures three effects of normality on causal judgment that have been observed in existing studies. (...)
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  • From covariation to causation: A causal power theory.Patricia Cheng - 1997 - Psychological Review 104 (2):367-405.
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  • Norm theory: Comparing reality to its alternatives.Daniel Kahneman & Dale T. Miller - 1986 - Psychological Review 93 (2):136-153.
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  • Foundations of a Probabilistic Theory of Causal Strength.Jan Sprenger - 2018 - Philosophical Review 127 (3):371-398.
    This paper develops axiomatic foundations for a probabilistic-interventionist theory of causal strength. Transferring methods from Bayesian confirmation theory, I proceed in three steps: I develop a framework for defining and comparing measures of causal strength; I argue that no single measure can satisfy all natural constraints; I prove two representation theorems for popular measures of causal strength: Pearl's causal effect measure and Eells' difference measure. In other words, I demonstrate these two measures can be derived from a set of plausible (...)
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  • Causal superseding.Jonathan F. Kominsky, Jonathan Phillips, Tobias Gerstenberg, David Lagnado & Joshua Knobe - 2015 - Cognition 137 (C):196-209.
    When agents violate norms, they are typically judged to be more of a cause of resulting outcomes. In this paper, we suggest that norm violations also affect the causality attributed to other agents, a phenomenon we refer to as "causal superseding." We propose and test a counterfactual reasoning model of this phenomenon in four experiments. Experiments 1 and 2 provide an initial demonstration of the causal superseding effect and distinguish it from previously studied effects. Experiment 3 shows that this causal (...)
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  • An improved probabilistic account of counterfactual reasoning.Christopher G. Lucas & Charles Kemp - 2015 - Psychological Review 122 (4):700-734.
    When people want to identify the causes of an event, assign credit or blame, or learn from their mistakes, they often reflect on how things could have gone differently. In this kind of reasoning, one considers a counterfactual world in which some events are different from their real-world counterparts and considers what else would have changed. Researchers have recently proposed several probabilistic models that aim to capture how people do (or should) reason about counterfactuals. We present a new model and (...)
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  • One and Done? Optimal Decisions From Very Few Samples.Edward Vul, Noah Goodman, Thomas L. Griffiths & Joshua B. Tenenbaum - 2014 - Cognitive Science 38 (4):599-637.
    In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and standard assumptions about optimality: People often appear to make decisions based on just one or a few samples from the appropriate posterior probability distribution, rather than using the full distribution. Although sampling-based approximations are a common way to implement Bayesian (...)
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  • Causal Responsibility and Counterfactuals.David A. Lagnado, Tobias Gerstenberg & Ro'I. Zultan - 2013 - Cognitive Science 37 (6):1036-1073.
    How do people attribute responsibility in situations where the contributions of multiple agents combine to produce a joint outcome? The prevalence of over-determination in such cases makes this a difficult problem for counterfactual theories of causal responsibility. In this article, we explore a general framework for assigning responsibility in multiple agent contexts. We draw on the structural model account of actual causation (e.g., Halpern & Pearl, 2005) and its extension to responsibility judgments (Chockler & Halpern, 2004). We review the main (...)
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  • Cause and Norm.Christopher Hitchcock & Joshua Knobe - 2009 - Journal of Philosophy 106 (11):587-612.
    Much of the philosophical literature on causation has focused on the concept of actual causation, sometimes called token causation. In particular, it is this notion of actual causation that many philosophical theories of causation have attempted to capture.2 In this paper, we address the question: what purpose does this concept serve? As we shall see in the next section, one does not need this concept for purposes of prediction or rational deliberation. What then could the purpose be? We will argue (...)
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  • (4 other versions)Causation.David Lewis - 1973 - Journal of Philosophy 70 (17):556-567.
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  • Beyond intuition and instinct blindness: Toward an evolutionary rigorous cognitive science.Leda Cosmides & John Tooby - 1994 - Cognition 50 (1-3):41-77.
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  • A counterfactual simulation model of causal judgments for physical events.Tobias Gerstenberg, Noah D. Goodman, David A. Lagnado & Joshua B. Tenenbaum - 2021 - Psychological Review 128 (5):936-975.
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  • A simple definition of ‘intentionally’.Tadeg Quillien & Tamsin C. German - 2021 - Cognition 214 (C):104806.
    Cognitive scientists have been debating how the folk concept of intentional action works. We suggest a simple account: people consider that an agent did X intentionally to the extent that X was causally dependent on how much the agent wanted X to happen (or not to happen). Combined with recent models of human causal cognition, this definition provides a good account of the way people use the concept of intentional action, and offers natural explanations for puzzling phenomena such as the (...)
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  • 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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  • When do we think that X caused Y?Tadeg Quillien - 2020 - Cognition 205:104410.
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  • Immoral Professors and Malfunctioning Tools: Counterfactual Relevance Accounts Explain the Effect of Norm Violations on Causal Selection.Jonathan Kominsky & Jonathan Phillips - 2019 - Cognitive Science 43 (11):e12792.
    Causal judgments are widely known to be sensitive to violations of both prescriptive norms (e.g., immoral events) and statistical norms (e.g., improbable events). There is ongoing discussion as to whether both effects are best explained in a unified way through changes in the relevance of counterfactual possibilities, or whether these two effects arise from unrelated cognitive mechanisms. Recent work has shown that moral norm violations affect causal judgments of agents, but not inanimate artifacts used by those agents. These results have (...)
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  • A counterfactual explanation for the action effect in causal judgment.Paul Henne, Laura Niemi, Ángel Pinillos, Felipe De Brigard & Joshua Knobe - 2019 - Cognition 190 (C):157-164.
    People’s causal judgments are susceptible to the action effect, whereby they judge actions to be more causal than inactions. We offer a new explanation for this effect, the counterfactual explanation: people judge actions to be more causal than inactions because they are more inclined to consider the counterfactual alternatives to actions than to consider counterfactual alternatives to inactions. Experiment 1a conceptually replicates the original action effect for causal judgments. Experiment 1b confirms a novel prediction of the new explanation, the reverse (...)
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  • (1 other version)Probabilities of causation: Three counterfactual interpretations and their identification.Judea Pearl - 1999 - Synthese 121 (1-2):93-149.
    According to common judicial standard, judgment in favor ofplaintiff should be made if and only if it is more probable than not thatthe defendant''s action was the cause for the plaintiff''s damage (or death). This paper provides formal semantics, based on structural models ofcounterfactuals, for the probability that event x was a necessary orsufficient cause (or both) of another event y. The paper then explicates conditions under which the probability of necessary (or sufficient)causation can be learned from statistical data, and (...)
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  • Two causal theories of counterfactual conditionals.Lance J. Rips - 2010 - Cognitive Science 34 (2):175-221.
    Bayes nets are formal representations of causal systems that many psychologists have claimed as plausible mental representations. One purported advantage of Bayes nets is that they may provide a theory of counterfactual conditionals, such as If Calvin had been at the party, Miriam would have left early. This article compares two proposed Bayes net theories as models of people's understanding of counterfactuals. Experiments 1-3 show that neither theory makes correct predictions about backtracking counterfactuals (in which the event of the if-clause (...)
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  • (1 other version)Counterfactual thinking and recency effects in causal judgment.Paul Henne, Aleksandra Kulesza, Karla Perez & Augustana Houcek - 2021 - Cognition 212 (C):104708.
    People tend to judge more recent events, relative to earlier ones, as the cause of some particular outcome. For instance, people are more inclined to judge that the last basket, rather than the first, caused the team to win the basketball game. This recency effect, however, reverses in cases of overdetermination: people judge that earlier events, rather than more recent ones, caused the outcome when the event is individually sufficient but not individually necessary for the outcome. In five experiments (N (...)
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