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  1. Judea Pearl, A General Identification Condition for Causal Effects.
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  2. Judea Pearl, Causes and Explanations: A Structural-Model Approach.
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  3. Judea Pearl, Direct and Indirect Effects.
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  4. Judea Pearl, Identifiability of Path-Specific Eff Ects.
    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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  5. Judea Pearl, Probabilities of Causation: Bounds and Identification.
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  6. Judea Pearl, The Logic of Counterfactuals in Causal Inference.
  7. Judea Pearl (2013). Erratum For: Structural Counterfactuals: A Brief Introduction, by Judea Pearl in Cognitive Science, 37 (6). Cognitive Science 37 (7).
     
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  8. Judea Pearl (2013). Structural Counterfactuals: A Brief Introduction. 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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  9. Judea Pearl (2011). The Structural Theory of Causation. In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oup Oxford.
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  10. Judea Pearl (2010). 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] Economics and Philosophy 26 (1):69-77.
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  11. Judea Pearl (2009). Causal Inference in Statistics. An Overview. Statistics Surveys 3:96-146.
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  12. Joseph Y. Halpern & Judea Pearl (2005). Causes and Explanations: A Structural-Model Approach. Part I: Causes. 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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  13. Judea Pearl (2003). Reply to Woodward. Economics and Philosophy 19 (2):341-344.
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  14. Judea Pearl (2001). Bayesianism and Causality, or, Why I Am Only a Half-Bayesian. In David Corfield & Jon Williamson (eds.), Foundations of Bayesianism. Kluwer Academic Publishers. 19--36.
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  15. Judea Pearl (2000). Causality: Models, Reasoning, and Inference. 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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  16. Judea Pearl (1999). Probabilities of Causation: Three Counterfactual Interpretations and Their Identification. 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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  17. David Galles & Judea Pearl (1998). An Axiomatic Characterization of Causal Counterfactuals. 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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  18. Adnan Darwiche & Judea Pearl (1997). On the Logic of Iterated Belief Revision. 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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  19. Moisés Goldszmidt & Judea Pearl (1996). Qualitative Probabilities for Default Reasoning, Belief Revision, and Causal Modeling. 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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  20. Judea Pearl (1992). Rejoinder to Comments on ``Reasoning with Belief Functions: An Analysis of Compatibility. International Journal of Approximate Reasoning 6 (3):425--443.
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  21. Hector Geffner & Judea Pearl (1990). A Framework for Reasoning with Defaults. In Kyburg Henry E., Loui Ronald P. & Carlson Greg N. (eds.), Knowledge Representation and Defeasible Reasoning. Kluwer. 69--87.
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  22. Judea Pearl (1990). Jeffrey's Rule, Passage of Experience, and Neo-Bayesianism. In Kyburg Henry E., Loui Ronald P. & Carlson Greg N. (eds.), Knowledge Representation and Defeasible Reasoning. Kluwer. 245--265.
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  23. Judea Pearl (1990). Reasoning with Belief Functions: An Analysis of Compatibility. International Journal of Approximate Reasoning 4:363--389.
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  24. Judea Pearl (1988). Probabilistic Reasoning in Intelligent Systems. 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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