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  1. Causality: Models, Reasoning, and Inference.Judea Pearl - 2000 - 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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  2.  43
    Probabilistic Reasoning in Intelligent Systems.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. 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.
    Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA judea{at}cs.ucla.edu' + u + '@' + d + ''//--> 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. Introduction Causal models: a review 2.1 Causal models 2.2 Syntax and semantics (...)
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  4. On the Logic of Iterated Belief Revision.Adnan Darwiche & Judea Pearl - 1997 - Artificial Intelligence 89:1-29.
    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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  5. Qualitative Probabilities for Default Reasoning, Belief Revision, and Causal Modeling.Moisés Goldszmidt & Judea Pearl - 1996 - Artificial Intelligence 84:57-112.
    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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  6. Causal Inference in Statistics. An Overview.Judea Pearl - 2009 - Statistics Surveys 3:96-146.
     
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  7. The Structural Theory of Causation.Judea Pearl - 2011 - In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press.
     
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  8.  54
    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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  9.  2
    Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.Judea Pearl - 1991 - Journal of Philosophy 88 (8):434-437.
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  10.  42
    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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  11. Causality.Judea Pearl - 2009 - 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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  12.  44
    Nancy Cartwright on Hunting Causes Hunting Causes and Using Them: Approaches in Philosophy and Economics , Nancy Cartwright. Cambridge University Press, 2008, X + 270 Pages. [REVIEW]Judea Pearl - 2010 - Economics and Philosophy 26 (1):69-77.
  13.  21
    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.
  14.  1
    Causality: Models, Reasoning and Inference.Christopher Hitchcock & Judea Pearl - 2001 - Philosophical Review 110 (4):639.
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  15.  78
    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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  16.  73
    The Logic of Counterfactuals in Causal Inference.Judea Pearl - manuscript
  17.  34
    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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  18. A Framework for Reasoning with Defaults.Hector Geffner & Judea Pearl - 1990 - In Kyburg Henry E., Loui Ronald P. & Carlson Greg N. (eds.), Knowledge Representation and Defeasible Reasoning. Kluwer Academic Publishers. pp. 69--87.
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  19.  3
    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.
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  20.  35
    A General Identification Condition for Causal Effects.Judea Pearl - manuscript
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  21.  32
    Direct and Indirect Effects.Judea Pearl - manuscript
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  22. Reasoning with Belief Functions: An Analysis of Compatibility.Judea Pearl - 1990 - International Journal of Approximate Reasoning 4:363--389.
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  23.  25
    Reply to Woodward.Judea Pearl - 2003 - Economics and Philosophy 19 (2):341-344.
    I thank Dr. Woodward for his illuminating review of my book Causality, for explicating so clearly the basic contributions of the book, and for giving me the opportunity to further clarify some aspects of the do-calculus, specifically those that pertain to the notion of intervention.
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  24.  21
    Probabilities of Causation: Bounds and Identification.Judea Pearl - manuscript
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  25.  16
    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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  26. Confounding Equivalence in Causal Inference.Judea Pearl & Azaria Paz - 2014 - Journal of Causal Inference 2 (1).
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  27. Causality. Models, Reasoning, and Inference.Judea Pearl - 2002 - Tijdschrift Voor Filosofie 64 (1):201-202.
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  28. Comment on Ding and Miratrix: “To Adjust or Not to Adjust?”.Judea Pearl - 2015 - Journal of Causal Inference 3 (1).
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  29. Conditioning on Post-Treatment Variables.Judea Pearl - 2015 - Journal of Causal Inference 3 (1).
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  30. Erratum For: Structural Counterfactuals: A Brief Introduction, by Judea Pearl in Cognitive Science, 37 (6).Judea Pearl - 2013 - Cognitive Science 37 (7).
     
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  31. Erratum to Is Scientific Knowledge Useful for Policy Analysis? A Peculiar Theorem Says: No [J Causal Inference DOI: 10.1515/Jci-2014-0017]. [REVIEW]Judea Pearl - 2014 - Journal of Causal Inference 2 (2).
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  32. Generalizing Experimental Findings.Judea Pearl - 2015 - Journal of Causal Inference 3 (2).
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  33. Graphoids Over Counterfactuals.Judea Pearl - 2014 - Journal of Causal Inference 2 (2).
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  34. Is Scientific Knowledge Useful for Policy Analysis? A Peculiar Theorem Says: No.Judea Pearl - 2014 - Journal of Causal Inference 2 (1).
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  35. Lord’s Paradox Revisited –.Judea Pearl - 2016 - Journal of Causal Inference 4 (2).
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  36. Rejoinder to Comments on ``Reasoning with Belief Functions: An Analysis of Compatibility.Judea Pearl - 1992 - International Journal of Approximate Reasoning 6 (3):425--443.
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  37. The Curse of Free-Will and the Paradox of Inevitable Regret.Judea Pearl - 2013 - Journal of Causal Inference 1 (2).
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  38. The Deductive Approach to Causal Inference.Judea Pearl - 2014 - Journal of Causal Inference 2 (2).
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  39. The Sure-Thing Principle.Judea Pearl - 2016 - Journal of Causal Inference.
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