Results for 'Causal models'

965 found
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  1. Quantum Causal Modelling.Fabio Costa & Sally Shrapnel - 2016 - New Journal of Physics 18 (6):063032.
    Causal modelling provides a powerful set of tools for identifying causal structure from observed correlations. It is well known that such techniques fail for quantum systems, unless one introduces 'spooky' hidden mechanisms. Whether one can produce a genuinely quantum framework in order to discover causal structure remains an open question. Here we introduce a new framework for quantum causal modelling that allows for the discovery of causal structure. We define quantum analogues for core features of (...)
     
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  2. Using causal models to integrate proximate and ultimate causation.Jun Otsuka - 2015 - Biology and Philosophy 30 (1):19-37.
    Ernst Mayr’s classical work on the nature of causation in biology has had a huge influence on biologists as well as philosophers. Although his distinction between proximate and ultimate causation recently came under criticism from those who emphasize the role of development in evolutionary processes, the formal relationship between these two notions remains elusive. Using causal graph theory, this paper offers a unified framework to systematically translate a given “proximate” causal structure into an “ultimate” evolutionary response, and illustrates (...)
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  3.  68
    A Causal Model Theory of the Meaning of Cause, Enable, and Prevent.Steven Sloman, Aron K. Barbey & Jared M. Hotaling - 2009 - Cognitive Science 33 (1):21-50.
    The verbs cause, enable, and prevent express beliefs about the way the world works. We offer a theory of their meaning in terms of the structure of those beliefs expressed using qualitative properties of causal models, a graphical framework for representing causal structure. We propose that these verbs refer to a causal model relevant to a discourse and that “A causes B” expresses the belief that the causal model includes a link from A to B. (...)
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  4.  17
    Abstracting Causal Models.Sander Beckers & Joseph Y. Halpern - 2019 - Proceedings of the 33Rd Aaai Conference on Artificial Intelligence.
    We consider a sequence of successively more restrictive definitions of abstraction for causal models, starting with a notion introduced by Rubenstein et al. (2017) called exact transformation that applies to probabilistic causal models, moving to a notion of uniform transformation that applies to deterministic causal models and does not allow differences to be hidden by the "right" choice of distribution, and then to abstraction, where the interventions of interest are determined by the map from (...)
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  5.  99
    Causal models and evidential pluralism in econometrics.Alessio Moneta & Federica Russo - 2014 - Journal of Economic Methodology 21 (1):54-76.
    Social research, from economics to demography and epidemiology, makes extensive use of statistical models in order to establish causal relations. The question arises as to what guarantees the causal interpretation of such models. In this paper we focus on econometrics and advance the view that causal models are ‘augmented’ statistical models that incorporate important causal information which contributes to their causal interpretation. The primary objective of this paper is to argue that (...)
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  6. Causal Models and the Logic of Counterfactuals.Jonathan Vandenburgh - manuscript
    Causal models show promise as a foundation for the semantics of counterfactual sentences. However, current approaches face limitations compared to the alternative similarity theory: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This paper addresses these difficulties using exogenous interventions, where causal interventions change the values of exogenous variables rather than structural equations. This model accommodates judgments about backtracking counterfactuals, extends to logically complex counterfactuals, and validates familiar (...)
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  7.  88
    From causal models to counterfactual structures.Joseph Y. Halpern - 2013 - Review of Symbolic Logic 6 (2):305-322.
    Galles & Pearl (l998) claimed that s [possible-worlds] framework.s framework. Recursive models are shown to correspond precisely to a subclass of (possible-world) counterfactual structures. On the other hand, a slight generalization of recursive models, models where all equations have unique solutions, is shown to be incomparable in expressive power to counterfactual structures, despite the fact that the Galles and Pearl arguments should apply to them as well. The problem with the Galles and Pearl argument is identified: an (...)
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  8. Causal Models and Cognitive Representations in Multiple Cue Judgment.Tommy Enkvist & Peter Juslin - 2007 - In McNamara D. S. & Trafton J. G., Proceedings of the 29th Annual Cognitive Science Society. Cognitive Science Society. pp. 977--982.
  9. Causal Models and Causal Relativism.Jennifer McDonald - 2025 - Synthese 205 (108):1 - 26.
    A promising development in the philosophy of causation analyzes actual causation using structural equation models, i.e., “causal models”. This paper carefully considers what it means for an interpreted model to be accurate of its target situation. These considerations show, first, that our existing understanding of accuracy is inadequate. Further, and more controversially, they show that any causal model analysis is committed to a kind of relativism – a view whereby causation is a three-part relation holding between (...)
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  10.  67
    Causal Models: How People Think About the World and its Alternatives.Steven Sloman - 2005 - Oxford, England: OUP.
    This book offers a discussion about how people think, talk, learn, and explain things in causal terms in terms of action and manipulation. Sloman also reviews the role of causality, causal models, and intervention in the basic human cognitive functions: decision making, reasoning, judgement, categorization, inductive inference, language, and learning.
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  11.  7
    Causal Models and the Ambiguity of Counterfactuals.Kok Yong Lee - 2015 - In Wiebe van der Hoek, Wesley H. Holliday & Wen-Fang Wang, Logic, Rationality, and Interaction 5th International Workshop, LORI 2015, Taipei, Taiwan, October 28-30, 2015. Proceedings. Springer. pp. 201-229.
    Counterfactuals are inherently ambiguous in the sense that the same counterfactual may be true under one mode of counterfactualization but false under the other. Many have regarded the ambiguity of counterfactuals as consisting in the distinction between forward-tracking and backtracking counterfactuals. This is incorrect since the ambiguity persists even in cases not involving backtracking counterfactualization. In this paper, I argue that causal modeling semantics has the resources enough for accounting for the ambiguity of counterfactuals. Specifically, we need to distinguish (...)
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  12.  37
    Causal Models with Constraints.Sander Beckers, Joseph Y. Halpern & Christopher Hitchcock - 2023 - Proceedings of the 2Nd Conference on Causal Learning and Reasoning.
    Causal models have proven extremely useful in offering formal representations of causal relationships between a set of variables. Yet in many situations, there are non-causal relationships among variables. For example, we may want variables LDL, HDL, and TOT that represent the level of low-density lipoprotein cholesterol, the level of lipoprotein high-density lipoprotein cholesterol, and total cholesterol level, with the relation LDL+HDL=TOT. This cannot be done in standard causal models, because we can intervene simultaneously on (...)
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  13. Quantum Causal Models, Faithfulness, and Retrocausality.Peter W. Evans - 2018 - British Journal for the Philosophy of Science 69 (3):745-774.
    Wood and Spekkens argue that any causal model explaining the EPRB correlations and satisfying the no-signalling constraint must also violate the assumption that the model faithfully reproduces the statistical dependences and independences—a so-called ‘fine-tuning’ of the causal parameters. This includes, in particular, retrocausal explanations of the EPRB correlations. I consider this analysis with a view to enumerating the possible responses an advocate of retrocausal explanations might propose. I focus on the response of Näger, who argues that the central (...)
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  14. Causal Modelling.Christopher Hitchcock - 2009 - In Helen Beebee, Christopher Hitchcock & Peter Menzies, The Oxford Handbook of Causation. Oxford University Press UK.
     
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  15.  60
    A causal model of post-traumatic stress disorder: disentangling predisposed from acquired neural abnormalities.Roee Admon, Mohammed R. Milad & Talma Hendler - 2013 - Trends in Cognitive Sciences 17 (7):337-347.
  16.  18
    Causal models of spatial categories.Jacob Feldman - 1993 - Behavioral and Brain Sciences 16 (2):244-245.
  17.  46
    Causal models and the acquisition of category structure.Michael R. Waldmann, Keith J. Holyoak & Angela Fratianne - 1995 - Journal of Experimental Psychology: General 124 (2):181.
  18. Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - New York: Cambridge University Press.
    Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.
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  19.  61
    Conditional Learning Through Causal Models.Jonathan Vandenburgh - 2020 - Synthese (1-2):2415-2437.
    Conditional learning, where agents learn a conditional sentence ‘If A, then B,’ is difficult to incorporate into existing Bayesian models of learning. This is because conditional learning is not uniform: in some cases, learning a conditional requires decreasing the probability of the antecedent, while in other cases, the antecedent probability stays constant or increases. I argue that how one learns a conditional depends on the causal structure relating the antecedent and the consequent, leading to a causal model (...)
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  20. Causality: models, reasoning and inference A review of Judea Pearl's Causality.Stephen F. LeRoy - 2002 - Journal of Economic Methodology 9 (1):100-102.
  21.  67
    Causal Models and the Ambiguity of Counterfactuals.Kok Yong Lee - 2015 - In Wiebe van der Hoek, Wesley H. Holliday & Wen-Fang Wang, Logic, Rationality, and Interaction 5th International Workshop, LORI 2015, Taipei, Taiwan, October 28-30, 2015. Proceedings. Springer. pp. 201-229.
    Counterfactuals are inherently ambiguous in the sense that the same counterfactual may be true under one mode of counterfactualization but false under the other. Many have regarded the ambiguity of counterfactuals as consisting in the distinction between forward-tracking and backtracking counterfactuals. This is incorrect since the ambiguity persists even in cases not involving backtracking counterfactualization. In this paper, I argue that causal modeling semantics has the resources enough for accounting for the ambiguity of counterfactuals. Specifically, we need to distinguish (...)
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  22.  19
    Equivalent Causal Models.Sander Beckers - 2021 - Proceedings of the Aaai Conference on Artificial Intelligence.
    The aim of this paper is to offer the first systematic exploration and definition of equivalent causal models in the context where both models are not made up of the same variables. The idea is that two models are equivalent when they agree on all "essential" causal information that can be expressed using their common variables. I do so by focussing on the two main features of causal models, namely their structural relations and (...)
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  23.  1
    Nondeterministic Causal Models.Sander Beckers - forthcoming - Proceedings of the 4Th Conference on Causal Learning and Reasoning.
    I generalize acyclic deterministic structural causal models to the nondeterministic case and argue that this offers an improved semantics for counterfactuals. The standard, deterministic, semantics developed by Halpern (and based on the initial proposal of Galles & Pearl) assumes that for each assignment of values to parent variables there is a unique assignment to their child variable, and it assumes that the actual world (an assignment of values to all variables of a model) specifies a unique counterfactual world (...)
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  24.  93
    Causal Models with Frequency Dependence.Ronald N. Giere - 1984 - Journal of Philosophy 81 (7):384.
  25.  31
    A causal model theory of categorization.Bob Rehder - 1999 - In Martin Hahn & S. C. Stoness, Proceedings of the 21st Annual Meeting of the Cognitive Science Society. Lawrence Erlbaum. pp. 595--600.
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  26. Causal models and space-time geometries.Zoltan Domotor - 1972 - Synthese 24 (1-2):5 - 57.
  27. Causal Models and Metaphysics - Part 1: Using Causal Models.Jennifer McDonald - 2024 - Philosophy Compass 19 (4).
    This paper provides a general introduction to the use of causal models in the metaphysics of causation, specifically structural equation models and directed acyclic graphs. It reviews the formal framework, lays out a method of interpretation capable of representing different underlying metaphysical relations, and describes the use of these models in analyzing causation.
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  28.  13
    Causal model progressions as a foundation for intelligent learning environments.Barbara Y. White & John R. Frederiksen - 1990 - Artificial Intelligence 42 (1):99-157.
  29.  66
    Appropriate causal models and the stability of causation.Joseph Y. Halpern - 2016 - Review of Symbolic Logic 9 (1):76-102.
  30.  96
    A Causal Model of Intentionality Judgment.Steven A. Sloman, Philip M. Fernbach & Scott Ewing - 2012 - Mind and Language 27 (2):154-180.
    We propose a causal model theory to explain asymmetries in judgments of the intentionality of a foreseen side-effect that is either negative or positive (Knobe, 2003). The theory is implemented as a Bayesian network relating types of mental states, actions, and consequences that integrates previous hypotheses. It appeals to two inferential routes to judgment about the intentionality of someone else's action: bottom-up from action to desire and top-down from character and disposition. Support for the theory comes from three experiments (...)
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  31.  30
    Graphical causal models of social adaptation and Hamilton’s rule.Wes Anderson - 2019 - Biology and Philosophy 34 (5):48.
    Part of Allen et al.’s criticism of Hamilton’s rule makes sense only if we are interested in social adaptation rather than merely social selection. Under the assumption that we are interested in casually modeling social adaptation, I illustrate how graphical causal models of social adaptation can be useful for predicting evolution by adaptation. I then argue for two consequences of this approach given some of the recent philosophical literature. I argue Birch’s claim that the proper way to understand (...)
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  32.  20
    Causal Models and Screening‐Off.Juhwa Park & Steven A. Sloman - 2016 - In Wesley Buckwalter & Justin Sytsma, Blackwell Companion to Experimental Philosophy. Malden, MA: Blackwell. pp. 450–462.
    This chapter explains the screening‐off rule in the psychological laboratory. The Markov assumption states that any variable in a set is independent in probability of all its ancestors in the set conditional on its own parents. The screening‐off rule is also critical to allow Bayes nets to make an inference of the state of an unknown variable in a causal structure from the states of other variables in that structure. The chapter examines which causal representations people use to (...)
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  33. Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Tijdschrift Voor Filosofie 64 (1):201-202.
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  34.  27
    A causal model for EPR.Nancy Cartwright & Mauricio Suárez - 2000 - Centre for Philosophy of Natural and Social Science.
    We present a causal model for the EPR correlations. In this model, or better framework for a model, causality is preserved by the direct propagation of causal influences between the wings of the experiment. We show that our model generates the same statistical results for EPR as orthodox quantum mechanics. We conclude that causality in quantum mechanics can not be ruled out on the basis of the EPR-Bell-Aspect correlations alone.
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  35. Causal Models and Metaphysics—Part 2: Interpreting Causal Models.Jennifer McDonald - 2024 - Philosophy Compass 19 (7):e13007.
    This paper addresses the question of what constitutes an apt interpreted model for the purpose of analyzing causation. I first collect universally adopted aptness principles into a basic account, flagging open questions and choice points along the way. I then explore various additional aptness principles that have been proposed in the literature but have not been widely adopted, the motivations behind their proposals, and the concerns with each that stand in the way of universal adoption. I conclude that the remaining (...)
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  36.  41
    Equivalence of causal models with latent variables.Peter Spirtes & Thomas Verma - unknown
    Peter Spirtes and Thomas Verma. Equivalence of Causal Models with Latent Variables.
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  37.  60
    Quantum causal models: the merits of the spirit of Reichenbach’s principle for understanding quantum causal structure.Robin Lorenz - 2022 - Synthese 200 (5):1-27.
    Through the introduction of his ‘common cause principle’ [The Direction of Time, 1956], Hans Reichenbach was the first to formulate a precise link relating causal claims to statements of probability. Despite some criticism, the principle has been hugely influential and successful—a pillar of scientific practice, as well as guiding our reasoning in everyday life. However, Bell’s theorem, taken in conjunction with quantum theory, challenges this principle in a fundamental sense at the microscopic level. For the same reason, the celebrated (...)
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  38. 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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  39.  1
    Actual Causation and Nondeterministic Causal Models.Sander Beckers - forthcoming - Proceedings of the 4Th Conference on Causal Learning and Reasoning, Pmlr.
    In (Beckers, 2025) I introduced nondeterministic causal models as a generalization of Pearl’s standard deterministic causal models. I here take advantage of the increased expressivity offered by these models to offer a novel definition of actual causation (that also applies to deterministic models). Instead of motivating the definition by way of (often subjective) intuitions about examples, I proceed by developing it based entirely on the unique function that it can fulfil in communicating and learning (...)
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  40. Causality: Models, reasoning and inference.Christopher Hitchcock - 2001 - Philosophical Review 110 (4):639-641.
    book reveiw van boek met gelijknamige titel van Judea Pearl.
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  41. A Priori Causal Models of Natural Selection.Elliott Sober - 2011 - Australasian Journal of Philosophy 89 (4):571 - 589.
    To evaluate Hume's thesis that causal claims are always empirical, I consider three kinds of causal statement: ?e1 caused e2 ?, ?e1 promoted e2 ?, and ?e1 would promote e2 ?. Restricting my attention to cases in which ?e1 occurred? and ?e2 occurred? are both empirical, I argue that Hume was right about the first two, but wrong about the third. Standard causal models of natural selection that have this third form are a priori mathematical truths. (...)
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  42.  45
    Causality, causal models, and social mechanisms.Daniel Steel - 2011 - In Ian Jarvie Jesus Zamora Bonilla, The Sage Handbook of the Philosophy of Social Sciences. SAGE Publications. pp. 288.
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  43. Actual Causation: Apt Causal Models and Causal Relativism.Jennifer McDonald - 2022 - Dissertation, The Graduate Center, Cuny
    This dissertation begins by addressing the question of when a causal model is apt for deciding questions of actual causation with respect to some target situation. I first provide relevant background about causal models, explain what makes them promising as a tool for analyzing actual causation, and motivate the need for a theory of aptness as part of such an analysis (Chapter 1). I then define what it is for a model on a given interpretation to be (...)
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  44.  35
    Causal Models in the History of Science.Osvaldo Pessoa Jr - 2005 - Croatian Journal of Philosophy 5 (14):263-274.
    The investigation of a method for postulating counterfactual histories of science has led to the development of a theory of science based on general units of knowledge, which are called “advances”. Advances are passed on from scientist to scientist, and may be seen as “causing” the appearance of other advances. This results in networks which may be analyzed in terms of probabilistic causal models, which are readily encodable in computer language. The probability for a set of advances to (...)
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  45.  37
    Mob Rules: Toward a Causal Model of Social Structure.Andrea Borghini & Marco J. Nathan - 2022 - American Philosophical Quarterly 59 (1):11-26.
    This essay enriches causal models capturing the propagation of prejudice, bias, and other aggregative social mechanisms, negative or positive. These explananda include the reinforcement of economic inequality, “mob-like” behavior, peer pressure, and the establishment of social norms. The stage is set by introducing various forms of redundant causation and discussing some difficulties with mainstream preemption. Next the main proposal extends current representations of aggregative social mechanisms in two respects. First, it is more nuanced, as it identifies three distinct (...)
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  46.  22
    Causal models versus reason models in Bayesian networks for legal evidence.Eivind Kolflaath & Christian Dahlman - 2022 - Synthese 200 (6).
    In this paper we compare causal models with reason models in the construction of Bayesian networks for legal evidence. In causal models, arrows in the network are drawn from causes to effects. In a reason model, the arrows are instead drawn towards the evidence, from factum probandum to factum probans. We explore the differences between causal models and reason models and observe several distinct advantages with reason models. Reason models are (...)
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  47. Learning to Learn Causal Models.Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum - 2010 - Cognitive Science 34 (7):1185-1243.
    Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these (...) models. The schema organizes the objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning. (shrink)
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  48.  59
    Learning from Non-Causal Models.Francesco Nappo - 2020 - Erkenntnis 87 (5):2419-2439.
    This paper defends the thesis of learning from non-causal models: viz. that the study of some model can prompt justified changes in one’s confidence in empirical hypotheses about a real-world target in the absence of any known or predicted similarity between model and target with regards to their causal features. Recognizing that we can learn from non-causal models matters not only to our understanding of past scientific achievements, but also to contemporary debates in the philosophy (...)
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  49.  20
    Latent Variables, Causal Models, and Overidentifying Constraints.Clark Glymour & Peter Spirtes - unknown
  50. Pretense, Counterfactuals, and Bayesian Causal Models: Why What Is Not Real Really Matters.Deena S. Weisberg & Alison Gopnik - 2013 - Cognitive Science 37 (7):1368-1381.
    Young children spend a large portion of their time pretending about non-real situations. Why? We answer this question by using the framework of Bayesian causal models to argue that pretending and counterfactual reasoning engage the same component cognitive abilities: disengaging with current reality, making inferences about an alternative representation of reality, and keeping this representation separate from reality. In turn, according to causal models accounts, counterfactual reasoning is a crucial tool that children need to plan for (...)
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