73 found
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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.  82
    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 - 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.  67
    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. 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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  7. 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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  8.  78
    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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  9.  14
    Causes and Explanations: A Structural-Model Approach. Part I: Causes.Judea Pearl - 2005 - British Journal for the Philosophy of Science 56 (4):843-887.
  10. 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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  11.  77
    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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  12.  36
    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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  13. Distributed Revision of Composite Beliefs.Judea Pearl - 1987 - Artificial Intelligence 33 (2):173-215.
  14.  1
    Fusion, Propagation, and Structuring in Belief Networks.Judea Pearl - 1986 - Artificial Intelligence 29 (3):241-288.
  15.  41
    Direct and Indirect Effects.Judea Pearl - manuscript
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  16. Causal Inference in Statistics. An Overview.Judea Pearl - 2009 - Statistics Surveys 3:96-146.
  17. Conditional Entailment: Bridging Two Approaches to Default Reasoning.Hector Geffner & Judea Pearl - 1992 - Artificial Intelligence 53 (2-3):209-244.
  18.  65
    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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  19.  13
    Does Obesity Shorten Life? Or is It the Soda? On Non-Manipulable Causes.Judea Pearl - 2018 - Journal of Causal Inference 6 (2).
    Non-manipulable factors, such as gender or race have posed conceptual and practical challenges to causal analysts. On the one hand these factors do have consequences, and on the other hand, they do not fit into the experimentalist conception of causation. This paper addresses this challenge in the context of public debates over the health cost of obesity, and offers a new perspective, based on the theory of Structural Causal Models.
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  20.  14
    Causes and Explanations: A Structural-Model Approach. Part II: Explanations.Judea Pearl - 2005 - British Journal for the Philosophy of Science 56 (4):889-911.
  21.  19
    Physical and Metaphysical Counterfactuals: Evaluating Disjunctive Actions.Judea Pearl - 2017 - Journal of Causal Inference 5 (2):1--10.
    The structural interpretation of counterfactuals as formulated in Balke and Pearl [.
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  22.  61
    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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  23. 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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  24.  5
    Axioms of Causal Relevance.David Galles & Judea Pearl - 1997 - Artificial Intelligence 97 (1-2):9-43.
  25.  31
    Probabilities of Causation: Bounds and Identification.Judea Pearl - manuscript
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  26.  41
    A General Identification Condition for Causal Effects.Judea Pearl - manuscript
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  27.  15
    On the Interpretation of D o ( X )Do.Judea Pearl - 2019 - Journal of Causal Inference 7 (1).
    This paper provides empirical interpretation of the dodo operator when applied to non-manipulable variables such as race, obesity, or cholesterol level. We view dodo 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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  28.  23
    Lord’s Paradox Revisited –.Judea Pearl - 2016 - Journal of Causal Inference 4 (2).
    Among the many peculiarities that were dubbed “paradoxes” by well meaning statisticians, the one reported by Frederic M. Lord in 1967 has earned a special status. Although it can be viewed, formally, as a version of Simpson’s paradox, its reputation has gone much worse. Unlike Simpson’s reversal, Lord’s is easier to state, harder to disentangle and, for some reason, it has been lingering for almost four decades, under several interpretations and re-interpretations, and it keeps coming up in new situations and (...)
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  29. 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.
  30.  13
    Nancy Cartwright on Hunting Causes: Reviews Symposium.Judea Pearl - 2010 - Economics and Philosophy 26 (1):69-77.
  31.  85
    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.
  32.  51
    The Curse of Free-Will and the Paradox of Inevitable Regret.Judea Pearl - 2013 - Journal of Causal Inference 1 (2).
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  33. Belief Networks Revisited.Judea Pearl - 1993 - Artificial Intelligence 59 (1-2):49-56.
  34.  1
    Temporal Constraint Networks.Rina Dechter, Itay Meiri & Judea Pearl - 1991 - Artificial Intelligence 49 (1-3):61-95.
  35.  22
    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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  36.  23
    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.
  37.  1
    Embracing Causality in Default Reasoning.Judea Pearl - 1988 - Artificial Intelligence 35 (2):259-271.
  38.  3
    On the Consistency of Defeasible Databases.Moisés Goldszmidt & Judea Pearl - 1991 - Artificial Intelligence 52 (2):121-149.
  39.  23
    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.  18
    The Sure-Thing Principle.Judea Pearl - 2016 - Journal of Causal Inference.
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  41. Reasoning with Belief Functions: An Analysis of Compatibility.Judea Pearl - 1990 - International Journal of Approximate Reasoning 4:363--389.
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  42.  1
    Review of Hunting Causes and Using Them: Approaches in Philosophy and Economics. [REVIEW]Judea Pearl - 2010 - Economics and Philosophy 26 (1):69-77.
  43.  11
    The Deductive Approach to Causal Inference.Judea Pearl - 2014 - Journal of Causal Inference 2 (2).
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  44. Evidential Reasoning Using Stochastic Simulation of Causal Models.Judea Pearl - 1987 - Artificial Intelligence 32 (2):245-257.
  45. On Evidential Reasoning in a Hierarchy of Hypotheses.Judea Pearl - 1986 - Artificial Intelligence 28 (1):9-15.
  46. The Relevance of Relevance.Devika Subramanian, Russell Greiner & Judea Pearl - 1997 - Artificial Intelligence 97 (1-2):1-5.
  47.  62
    Sufficient Causes: On Oxygen, Matches, and Fires.Judea Pearl - 2019 - Journal of Causal Inference 7 (2).
    We demonstrate how counterfactuals can be used to compute the probability that one event was/is a sufficient cause of another, and how counterfactuals emerge organically from basic scientific knowledge, rather than manipulative experiments. We contrast this demonstration with the potential outcome framework and address the distinction between causes and enablers.
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  48.  6
    Asymptotic Properties of Minimax Trees and Game-Searching Procedures.Judea Pearl - 1980 - Artificial Intelligence 14 (2):113-138.
  49. The Logic of Counterfactuals in Causal Inference.Judea Pearl - manuscript
  50.  4
    Confounding Equivalence in Causal Inference.Judea Pearl & Azaria Paz - 2014 - Journal of Causal Inference 2 (1).
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