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  1. Judgment under Uncertainty: Heuristics and Biases.Amos Tversky & Daniel Kahneman - 1974 - Science 185 (4157):1124-1131.
    This article described three heuristics that are employed in making judgements under uncertainty: representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value (...)
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  • The Foundations of Causal Decision Theory.James M. Joyce - 1999 - Cambridge University Press.
    This book defends the view that any adequate account of rational decision making must take a decision maker's beliefs about causal relations into account. The early chapters of the book introduce the non-specialist to the rudiments of expected utility theory. The major technical advance offered by the book is a 'representation theorem' that shows that both causal decision theory and its main rival, Richard Jeffrey's logic of decision, are both instances of a more general conditional decision theory. The book solves (...)
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  • Causation, Prediction, and Search.Peter Spirtes, Clark Glymour, Scheines N. & Richard - 1993 - Mit Press: Cambridge.
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  • Causal decision theory.Paul Weirich - 2009 - Stanford Encyclopedia of Philosophy.
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  • 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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  • Probabilistic causality and Simpson's paradox.Richard Otte - 1985 - Philosophy of Science 52 (1):110-125.
    This paper discusses Simpson's paradox and the problem of positive relevance in probabilistic causality. It is argued that Cartwright's solution to Simpson's paradox fails because it ignores one crucial form of the paradox. After clarifying different forms of the paradox, it is shown that any adequate solution to the paradox must allow a cause to be both a negative cause and a positive cause of..
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  • Conditioning and intervening.Christopher Meek & Clark Glymour - 1994 - British Journal for the Philosophy of Science 45 (4):1001-1021.
    We consider the dispute between causal decision theorists and evidential decision theorists over Newcomb-like problems. We introduce a framework relating causation and directed graphs developed by Spirtes et al. (1993) and evaluate several arguments in this context. We argue that much of the debate between the two camps is misplaced; the disputes turn on the distinction between conditioning on an event E as against conditioning on an event I which is an action to bring about E. We give the essential (...)
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  • Probabilistic causality and the question of transitivity.Ellery Eells & Elliott Sober - 1983 - Philosophy of Science 50 (1):35-57.
    After clarifying the probabilistic conception of causality suggested by Good (1961-2), Suppes (1970), Cartwright (1979), and Skyrms (1980), we prove a sufficient condition for transitivity of causal chains. The bearing of these considerations on the units of selection problem in evolutionary theory and on the Newcomb paradox in decision theory is then discussed.
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  • Judgment Under Uncertainty: Heuristics and Biases.Daniel Kahneman, Paul Slovic & Amos Tversky (eds.) - 1982 - Cambridge University Press.
    The thirty-five chapters in this book describe various judgmental heuristics and the biases they produce, not only in laboratory experiments but in important...
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  • Thinking, Fast and Slow.Daniel Kahneman - 2011 - New York: New York: Farrar, Straus and Giroux.
    In the international bestseller, Thinking, Fast and Slow, Daniel Kahneman, the renowned psychologist and winner of the Nobel Prize in Economics, takes us on a groundbreaking tour of the mind and explains the two systems that drive the way we think. System 1 is fast, intuitive, and emotional; System 2 is slower, more deliberative, and more logical. The impact of overconfidence on corporate strategies, the difficulties of predicting what will make us happy in the future, the profound effect of cognitive (...)
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  • Ifs. Conditionals, Belief, Decision, Chance, and Time.Donald Nute - 1984 - Tijdschrift Voor Filosofie 46 (1):181-182.
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  • Critical Commentary on Unto Others.David Sloan Wilson & Elliott Sober - 2002 - Philosophy and Phenomenological Research 65 (3):697-701.
    Altruism has both an evolutionary and a psychological meaning. As the term is used in evolutionary theory, a trait is deemed altruistic if it reduces the fitness of the actor and enhances the fitness of someone else. In its psychological sense, the thesis that we have altruistic ultimate motives asserts that we care about the welfare of others, not just as a means of enhancing our own well-being, but as an end in itself. In Unto Others (hereafter UO), we consider (...)
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  • Experimental and quasi-experimental designs for generalized causal inference.William R. Shadish - 2001 - Boston: Houghton Mifflin. Edited by Thomas D. Cook & Donald Thomas Campbell.
    Sections include: experiments and generalised causal inference; statistical conclusion validity and internal validity; construct validity and external validity; quasi-experimental designs that either lack a control group or lack pretest observations on the outcome; quasi-experimental designs that use both control groups and pretests; quasi-experiments: interrupted time-series designs; regresssion discontinuity designs; randomised experiments: rationale, designs, and conditions conducive to doing them; practical problems 1: ethics, participation recruitment and random assignment; practical problems 2: treatment implementation and attrition; generalised causal inference: a grounded theory; (...)
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  • Causality and causal modelling in the social sciences.Federica Russo - 2009 - Springer, Dordrecht.
    The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a bygone’ nor ‘another fetish of modern science’; it still occupies a large part of the current debate in philosophy and the sciences. This investigation into causal modelling presents the rationale of causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, nor of regularity neither invariance, thus breaking down the dominant (...)
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  • The rule of adjunction and reasonable inference.Henry E. Kyburg - 1997 - Journal of Philosophy 94 (3):109-125.
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  • Causal laws and effective strategies.Nancy Cartwright - 1979 - Noûs 13 (4):419-437.
    La autora presenta algunas criticas generales al proyecto de reducir las leyes causales a probabilidades. Además, muestra que las leyes causales son imprescindibles para poder diferenciar las strategias efectivas de las que no lo son y da un criterio para considerar cuando podemos deducir causalidad a través de datos estadísticos.
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  • The Rule of Adjunction and Reasonable Inference.Henry E. Kyburg - 1997 - Journal of Philosophy 94 (3):109-125.
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  • Causality.Judea Pearl - 2000 - New York: Cambridge University Press.
    Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections (...)
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  • Causal necessity: a pragmatic investigation of the necessity of laws.Brian Skyrms - 1980 - New Haven: Yale University Press.
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  • .Daniel Kahneman & Shane Frederick - 2002 - Cambridge University Press.
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  • A Treatise on the Principles of Human Knowledge.George Berkeley - 1710 - Aaron Rhames. Edited by G. J. Warnock.
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  • Review: The Grand Leap; Reviewed Work: Causation, Prediction, and Search. [REVIEW]Peter Spirtes, Clark Glymour & Richard Scheines - 1996 - British Journal for the Philosophy of Science 47 (1):113-123.
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  • Causality in Macroeconomics.Kevin D. Hoover & Kevin D. Autor Hoover - 2001 - Cambridge University Press.
    Causality in Macroeconomics examines causality while taking macroeconomics seriously. A pragmatic and realistic philosophy is joined to a macroeconomic foundation that refines Herbert Simon's well-known work on causal order to make a case for a structural approach to causality. The structural approach is used to understand modern rational expectations models, regime switching models, Granger causality, vector autoregressions, the Lucas critique, and concept exogeneity. Techniques of causal inference based on patterns of stability and instability in the face of identified regime changes (...)
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  • A Treatise Concerning the Principles of Human Knowledge.George Berkeley - 1901 - The Monist 11:637.
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  • Causal Necessity: A Pragmatic Investigation of the Necessity of Laws.C. A. Hooker - 1984 - Noûs 18 (3):517-521.
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  • Simpson’s Paradox.Gary Malinas - 2001 - The Monist 84 (2):265-283.
    This article examines Simpson's paradox as applied to the theory of probabilites and percentages. The author discusses possible flaws in the paradox and compares it to the Sure Thing Principle, statistical inference, causal inference and probabilistic analyses of causation.
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  • Précis of Unto Others.David Sloan Wilson & Elliott Sober - 2002 - Philosophy and Phenomenological Research 65 (3):681-684.
    It is a challenge to explain how evolutionary altruism can evolve by the process of natural selection, since altruists in a group will be less fit than the selfish individuals in the same group who receive benefits but do not make donations of their own. Darwin proposed a theory of group selection to solve this puzzle. Very simply, even though altruists are less fit than selfish individuals within any single group, groups of altruists are more fit than groups of selfish (...)
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  • Critical notice.Clark Glymour - 1976 - Canadian Journal of Philosophy 6 (1):161-175.
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  • The Foundations of Causal Decision Theory.Isaac Levi & James M. Joyce - 2000 - Journal of Philosophy 97 (7):387.
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  • Causal Necessity: A Pragmatic Investigation of the Necessity of Laws.Richard C. Jeffrey - 1980 - Journal of Symbolic Logic 50 (2):557-558.
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