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Judea Pearl [60]J. Leavitt Pearl [6]J. Pearl [3]Joel Pearl [2]
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  1. 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.
  2. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.Judea Pearl - 1988 - Morgan Kaufmann.
    The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
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  3. 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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  4. Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Tijdschrift Voor Filosofie 64 (1):201-202.
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  5.  99
    Causality: Models, Reasoning and Inference.Christopher Hitchcock & Judea Pearl - 2001 - Philosophical Review 110 (4):639.
    Judea Pearl has been at the forefront of research in the burgeoning field of causal modeling, and Causality is the culmination of his work over the last dozen or so years. For philosophers of science with a serious interest in causal modeling, Causality is simply mandatory reading. Chapter 2, in particular, addresses many of the issues familiar from works such as Causation, Prediction and Search by Peter Spirtes, Clark Glymour, and Richard Scheines. But philosophers with a more general interest in (...)
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  6. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.J. Pearl, F. Bacchus, P. Spirtes, C. Glymour & R. Scheines - 1988 - Synthese 104 (1):161-176.
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  7.  13
    The book of why: the new science of cause and effect.Judea Pearl - 2018 - New York: Basic Books. Edited by Dana Mackenzie.
    Everyone has heard the claim, "Correlation does not imply causation." What might sound like a reasonable dictum metastasized in the twentieth century into one of science's biggest obstacles, as a legion of researchers became unwilling to make the claim that one thing could cause another. Even two decades ago, asking a statistician a question like "Was it the aspirin that stopped my headache?" would have been like asking if he believed in voodoo, or at best a topic for conversation at (...)
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  8. Causes and explanations: A structural-model approach. Part I: Causes.Joseph Y. Halpern & Judea Pearl - 2005 - British Journal for the Philosophy of Science 56 (4):843-887.
    We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficulties in the traditional account.
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  9. On the logic of iterated belief revision.Adnan Darwiche & Judea Pearl - 1997 - Artificial Intelligence 89 (1-2):1-29.
    We show in this paper that the AGM postulates are too weak to ensure the rational preservation of conditional beliefs during belief revision, thus permitting improper responses to sequences of observations. We remedy this weakness by proposing four additional postulates, which are sound relative to a qualitative version of probabilistic conditioning. Contrary to the AGM framework, the proposed postulates characterize belief revision as a process which may depend on elements of an epistemic state that are not necessarily captured by a (...)
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  10. Causes and explanations: A structural-model approach.Judea Pearl - manuscript
    We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficultiesn in the traditional account.
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  11.  19
    Causes and Explanations: A Structural-Model Approach. Part I: Causes.Judea Pearl - 2005 - British Journal for the Philosophy of Science 56 (4):843-887.
    We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficulties in the traditional account. 1. Introduction2. Causal models: a review2.1Causal models2.2Syntax and semantics3. The definition of cause4. Examples5. A more refined definition6. DiscussionAAppendix: Some Technical IssuesA.1The active causal processA.2A closer look at AC2(b)A.3Causality with infinitely many variablesA.4Causality in nonrecursive (...)
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  12.  5
    Fusion, propagation, and structuring in belief networks.Judea Pearl - 1986 - Artificial Intelligence 29 (3):241-288.
  13.  7
    Network-based heuristics for constraint-satisfaction problems.Rina Dechter & Judea Pearl - 1987 - Artificial Intelligence 34 (1):1-38.
  14.  57
    Qualitative probabilities for default reasoning, belief revision, and causal modeling.Moisés Goldszmidt & Judea Pearl - 1996 - Artificial Intelligence 84 (1-2):57-112.
    This paper presents a formalism that combines useful properties of both logic and probabilities. Like logic, the formalism admits qualitative sentences and provides symbolic machinery for deriving deductively closed beliefs and, like probability, it permits us to express if-then rules with different levels of firmness and to retract beliefs in response to changing observations. Rules are interpreted as order-of-magnitude approximations of conditional probabilities which impose constraints over the rankings of worlds. Inferences are supported by a unique priority ordering on rules (...)
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  15.  19
    Temporal constraint networks.Rina Dechter, Itay Meiri & Judea Pearl - 1991 - Artificial Intelligence 49 (1-3):61-95.
  16. An axiomatic characterization of causal counterfactuals.David Galles & Judea Pearl - 1998 - Foundations of Science 3 (1):151-182.
    This paper studies the causal interpretation of counterfactual sentences using a modifiable structural equation model. It is shown that two properties of counterfactuals, namely, composition and effectiveness, are sound and complete relative to this interpretation, when recursive (i.e., feedback-less) models are considered. Composition and effectiveness also hold in Lewis's closest-world semantics, which implies that for recursive models the causal interpretation imposes no restrictions beyond those embodied in Lewis's framework. A third property, called reversibility, holds in nonrecursive causal models but not (...)
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  17.  11
    Conditional entailment: Bridging two approaches to default reasoning.Hector Geffner & Judea Pearl - 1992 - Artificial Intelligence 53 (2-3):209-244.
  18. Causes and Explanations: A Structural-Model Approach. Part II: Explanations.Joseph Y. Halpern & Judea Pearl - 2005 - British Journal for the Philosophy of Science 56 (4):889-911.
    We propose new definitions of (causal) explanation, using structural equations to model counterfactuals. The definition is based on the notion of actual cause, as defined and motivated in a companion article. Essentially, an explanation is a fact that is not known for certain but, if found to be true, would constitute an actual cause of the fact to be explained, regardless of the agent's initial uncertainty. We show that the definition handles well a number of problematic examples from the literature.
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  19.  3
    Distributed revision of composite beliefs.Judea Pearl - 1987 - Artificial Intelligence 33 (2):173-215.
  20.  12
    Tree clustering for constraint networks.Rina Dechter & Judea Pearl - 1989 - Artificial Intelligence 38 (3):353-366.
  21.  78
    Structural Counterfactuals: A Brief Introduction.Judea Pearl - 2013 - Cognitive Science 37 (6):977-985.
    Recent advances in causal reasoning have given rise to a computational model that emulates the process by which humans generate, evaluate, and distinguish counterfactual sentences. Contrasted with the “possible worlds” account of counterfactuals, this “structural” model enjoys the advantages of representational economy, algorithmic simplicity, and conceptual clarity. This introduction traces the emergence of the structural model and gives a panoramic view of several applications where counterfactual reasoning has benefited problem areas in the empirical sciences.
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  22.  47
    Direct and indirect effects.Judea Pearl - manuscript
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  23. Causal inference in statistics. An overview.Judea Pearl - 2009 - Statistics Surveys 3:96-146.
  24.  24
    Causes and Explanations: A Structural-Model Approach. Part II: Explanations.Judea Pearl - 2005 - British Journal for the Philosophy of Science 56 (4):889-911.
    We propose new definitions of (causal) explanation, using structural equations to model counterfactuals. The definition is based on the notion of actual cause, as defined and motivated in a companion article. Essentially, an explanation is a fact that is not known for certain but, if found to be true, would constitute an actual cause of the fact to be explained, regardless of the agent's initial uncertainty. We show that the definition handles well a number of problematic examples from the literature. (...)
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  25.  7
    Structure identification in relational data.Rina Dechter & Judea Pearl - 1992 - Artificial Intelligence 58 (1-3):237-270.
  26.  6
    Embracing causality in default reasoning.Judea Pearl - 1988 - Artificial Intelligence 35 (2):259-271.
  27.  6
    Evidential reasoning using stochastic simulation of causal models.Judea Pearl - 1987 - Artificial Intelligence 32 (2):245-257.
  28.  73
    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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  29.  12
    Axioms of causal relevance.David Galles & Judea Pearl - 1997 - Artificial Intelligence 97 (1-2):9-43.
  30.  11
    Asymptotic properties of minimax trees and game-searching procedures.Judea Pearl - 1980 - Artificial Intelligence 14 (2):113-138.
  31.  8
    On the consistency of defeasible databases.Moisés Goldszmidt & Judea Pearl - 1991 - Artificial Intelligence 52 (2):121-149.
  32.  3
    Searching for an optimal path in a tree with random costs.Richard M. Karp & Judea Pearl - 1983 - Artificial Intelligence 21 (1-2):99-116.
  33.  37
    Probabilities of causation: Bounds and identification.Judea Pearl - manuscript
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  34.  5
    On evidential reasoning in a hierarchy of hypotheses.Judea Pearl - 1986 - Artificial Intelligence 28 (1):9-15.
  35.  7
    On the nature of pathology in game searching.Judea Pearl - 1983 - Artificial Intelligence 20 (4):427-453.
  36.  5
    The Structural Theory of Causation.Judea Pearl - 2011 - In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press.
  37.  20
    On the Interpretation of do(x)do(x).Judea Pearl - 2019 - Journal of Causal Inference 7 (1).
    This paper provides empirical interpretation of the do(x)do(x) operator when applied to non-manipulable variables such as race, obesity, or cholesterol level. We view do(x)do(x) as an ideal intervention that provides valuable information on the effects of manipulable variables and is thus empirically testable. We draw parallels between this interpretation and ways of enabling machines to learn effects of untried actions from those tried. We end with the conclusion that researchers need not distinguish manipulable from non-manipulable variables; both types are equally (...)
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  38.  8
    A minimax algorithm better than alpha-beta? Yes and No.Igor Roizen & Judea Pearl - 1983 - Artificial Intelligence 21 (1-2):199-220.
  39.  55
    Radical empiricism and machine learning research.Judea Pearl - 2021 - Journal of Causal Inference 9 (1):78-82.
    I contrast the “data fitting” vs “data interpreting” approaches to data science along three dimensions: Expediency, Transparency, and Explainability. “Data fitting” is driven by the faith that the secret to rational decisions lies in the data itself. In contrast, the data-interpreting school views data, not as a sole source of knowledge but as an auxiliary means for interpreting reality, and “reality” stands for the processes that generate the data. I argue for restoring balance to data science through a task-dependent symbiosis (...)
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  40.  11
    Question of time: Freud in the light of Heidegger's temporality.Joel Pearl (ed.) - 2013 - New York, NY: Rodopi.
    In A Question of Time, Joel Pearl offers a new reading of the foundations of psychoanalytic thought, indicating the presence of an essential lacuna that has been integral to psychoanalysis since its inception. Pearl returns to the moment in which psychoanalysis was born, demonstrating how Freud had overlooked one of the most principal issues pertinent to his method: the question of time. The book shows that it is no coincidence that Freud had never methodically and thoroughly discussed time and that (...)
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  41.  4
    Belief networks revisited.Judea Pearl - 1993 - Artificial Intelligence 59 (1-2):49-56.
  42.  46
    A general identification condition for causal effects.Judea Pearl - manuscript
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  43.  24
    Identifiability of path-specific eff ects.Judea Pearl - manuscript
    UCLA Cognitive Systems Laboratory, Technical Report (R-321), June 2005. In Proceedings of International Joint Conference on Artificial Intelligen ce, Edinburgh, Scotland, August 2005.
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  44.  7
    Probabilistic analysis of the complexity of A∗.Nam Huyn, Rina Dechter & Judea Pearl - 1980 - Artificial Intelligence 15 (3):241-254.
  45.  33
    Jeffrey's rule, passage of experience, and Neo-Bayesianism.Judea Pearl - 1990 - In Kyburg Henry E., Loui Ronald P. & Carlson Greg N. (eds.), Knowledge Representation and Defeasible Reasoning. Kluwer Academic Publishers. pp. 245--265.
  46. Bayesianism and causality, or, why I am only a half-Bayesian.Judea Pearl - 2001 - In David Corfield & Jon Williamson (eds.), Foundations of Bayesianism. Kluwer Academic Publishers. pp. 19--36.
  47. Reasoning with belief functions: An analysis of compatibility.Judea Pearl - 1990 - International Journal of Approximate Reasoning 4:363--389.
     
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  48.  27
    Popitz’s Imaginative Variation on Power as Model for Critical Phenomenology.J. Leavitt Pearl - 2018 - Human Studies 41 (3):475-483.
    Heinrich Popitz’s Phenomena of Power aims to uncover power as “a universal component in the genesis and operation of human societies”. In order to uncover this “universal” concept of power, Popitz employs Husserl’s method of the “imaginative variation” [Phantasievariation]. Yet, contrary to phenomenology’s traditionally descriptive posture, Phenomena of Power’s project is at once descriptive and normative—seeking not only to describe power, but to also describe the way in which power can be remade. In the present paper it is argued that (...)
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  49.  15
    After Finitude and the Question of Phenomenological Givenness.J. Leavitt Pearl - 2018 - PhaenEx 12 (2):13-36.
    Quentin Meillassoux’s 2006 After Finitude offered a sharp critique of the phenomenological project, charging that phenomenology was one of the “two principal media” of correlationism—ultimately reducible to an “extreme idealism.” Meillassoux grounds this accusation in an account of givenness that presupposes that “every variety of givenness” finds its genesis within the positing of the subject. However, this critique fails to hit its mark precisely because it presupposes an account of intuitive givenness that is entirely foreign to the phenomenological project. Quite (...)
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  50.  4
    The relevance of relevance.Devika Subramanian, Russell Greiner & Judea Pearl - 1997 - Artificial Intelligence 97 (1-2):1-5.
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