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16
Causes and explanations: A structuralmodel approach
 In Proceedings IJCAI01
, 2001
"... 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 ..."
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Cited by 118 (9 self)
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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
Axiomatizing causal reasoning
 Uncertainty in Artificial Intelligence
, 1998
"... Causal models defined in terms of a collection of equations, as defined by Pearl, are axiomatized here. Axiomatizations are provided for three successively more general classes of causal models: (1) the class of recursive theories (those without feedback), (2) the class of theories where the solutio ..."
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Cited by 65 (5 self)
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Causal models defined in terms of a collection of equations, as defined by Pearl, are axiomatized here. Axiomatizations are provided for three successively more general classes of causal models: (1) the class of recursive theories (those without feedback), (2) the class of theories where the solutions to the equations are unique, (3) arbitrary theories (where the equations may not have solutions and, if they do, they are not necessarily unique). It is shown that to reason about causality in the most general third class, we must extend the language used by Galles and Pearl (1997, 1998). In addition, the complexity of the decision procedures is characterized for all the languages and classes of models considered. 1.
Reasoning With Cause And Effect
, 1999
"... This paper summarizes basic concepts and principles that I have found to be useful in dealing with causal reasoning. The paper is written as a companion to a lecture under the same title, to be presented at IJCAI99, and is intended to supplement the lecture with technical details and pointers to mo ..."
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Cited by 36 (0 self)
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This paper summarizes basic concepts and principles that I have found to be useful in dealing with causal reasoning. The paper is written as a companion to a lecture under the same title, to be presented at IJCAI99, and is intended to supplement the lecture with technical details and pointers to more elaborate discussions in the literature. The ruling conception will be to treat causation as a computational schema devised to identify the invariant relationships in the environment, so as to facilitate reliable prediction of the effect of actions. This conception, as well as several of its satellite principles and tools, has been guiding paradigm for several research communities in AI, most notably those connected with causal discovery, troubleshooting, planning under uncertainty and modeling the behavior of physical systems. My hopes are to encourage a broader and more effective usage of causal modeling by explicating these common principles in simple and familiar mathematical form. Af...
Structurebased causes and explanations in the independent choice logic
 Proceedings UAI2003
, 2003
"... This paper is directed towards combining Pearl’s structuralmodel approach to causal reasoning with highlevel formalisms for reasoning about actions. More precisely, we present a combination of Pearl’s structuralmodel approach with Poole’s independent choice logic. We show how probabilistic theor ..."
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Cited by 13 (6 self)
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This paper is directed towards combining Pearl’s structuralmodel approach to causal reasoning with highlevel formalisms for reasoning about actions. More precisely, we present a combination of Pearl’s structuralmodel approach with Poole’s independent choice logic. We show how probabilistic theories in the independent choice logic can be mapped to probabilistic causal models. This mapping provides the independent choice logic with appealing concepts of causality and explanation from the structuralmodel approach. We illustrate this along Halpern and Pearl’s sophisticated notions of actual cause, explanation, and partial explanation. Furthermore, this mapping also adds firstorder modeling capabilities and explicit actions to the structuralmodel approach.
Integrating Bayesian Networks into KnowledgeIntensive CBR
 Proceedings of AAAI Workshop on CBR Integration
, 1998
"... In this paper we propose an approach to knowledge intensive CBR, where explanations are generated from a domain model consisting partly of a semantic network and partly of a Bayesian network (BN). The BN enables learning within this domain model based on the observed data. The domain model is used t ..."
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Cited by 10 (5 self)
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In this paper we propose an approach to knowledge intensive CBR, where explanations are generated from a domain model consisting partly of a semantic network and partly of a Bayesian network (BN). The BN enables learning within this domain model based on the observed data. The domain model is used to focus the retrieval and reuse of past cases, as well as the indexing when learning a new case. Essentially, the BNpowered submodel works in parallel with the semantic network model to generate a statistically sound contribution to case indexing, retrieval and explanation. 1. Introduction and
Causes and Explanations in the StructuralModel Approach: Tractable Cases
 IN PROC. EIGHTEENTH CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE (UAI 2002
, 2002
"... In this paper, we continue our research on the algorithmic aspects of Halpern and Pearl's causes and explanations in the structuralmodel approach. To this end, we present new characterizations of weak causes for certain classes of causal models, which show that under suitable restrictions deciding ..."
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Cited by 10 (3 self)
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In this paper, we continue our research on the algorithmic aspects of Halpern and Pearl's causes and explanations in the structuralmodel approach. To this end, we present new characterizations of weak causes for certain classes of causal models, which show that under suitable restrictions deciding causes and explanations is tractable. To our knowledge, these are the first explicit tractability results for the structuralmodel approach.
Complexity Results for Explanations in the StructuralModel Approach
 Institut für Informationssysteme
, 2002
"... We analyze the computational complexity of Halpern and Pearl's (causal) explanations in the structuralmodel approach, which are based on their notions of weak and actual causality. In particular, we give a precise picture of the complexity of deciding explanations, partial explanations, and ..."
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Cited by 6 (5 self)
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We analyze the computational complexity of Halpern and Pearl's (causal) explanations in the structuralmodel approach, which are based on their notions of weak and actual causality. In particular, we give a precise picture of the complexity of deciding explanations, partial explanations, and partial explanations, and of computing the explanatory power of partial explanations.
Minimal Sufficient Explanations for Factored Markov Decision Processes
 In Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS’09
, 2009
"... Explaining policies of Markov Decision Processes (MDPs) is complicated due to their probabilistic and sequential nature. We present a technique to explain policies for factored MDP by populating a set of domainindependent templates. We also present a mechanism to determine a minimal set of template ..."
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Cited by 3 (1 self)
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Explaining policies of Markov Decision Processes (MDPs) is complicated due to their probabilistic and sequential nature. We present a technique to explain policies for factored MDP by populating a set of domainindependent templates. We also present a mechanism to determine a minimal set of templates that, viewed together, completely justify the policy. Our explanations can be generated automatically at runtime with no additional effort required from the MDP designer. We demonstrate our technique using the problems of advising undergraduate students in their course selection and assisting people with dementia in completing the task of handwashing. We also evaluate our explanations for courseadvising through a user study involving students.
Explaining Predictions in Bayesian Networks and Influence Diagrams
 In Proc. of the AAAI 1998 Spring Symposium Series: Interactive and MixedInitiative DecisionTheoretic Systems
, 1998
"... As Bayesian Networks and Influence Diagrams are being used more and more widely, the importance of an efficient explanation mechanism becomes more apparent. We focus on predictive explanations, the ones designed to explain predictions and recommendations of probabilistic systems. We analyze the issu ..."
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Cited by 2 (0 self)
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As Bayesian Networks and Influence Diagrams are being used more and more widely, the importance of an efficient explanation mechanism becomes more apparent. We focus on predictive explanations, the ones designed to explain predictions and recommendations of probabilistic systems. We analyze the issues involved in defining, computing and evaluating such explanations and present an algorithm to compute them. Introduction As knowledgebased reasoning systems begin addressing realworld problems, they are often designed to be used not by experts but by people unfamiliar with the domain. Such people are unlikely to accept systems prediction or advice without some explanation. In addition, the systems ever increasing size makes their computations more and more difficult to follow even for their creators. This situation makes an explanation mechanism critical for making these systems useful and widely accepted. Probabilistic systems, such as Bayesian Networks (Pearl 1988) and Influence Di...
Simplifying Explanations in Bayesian Belief Networks
 International Journal of Uncertainty, Fuzziness and KnowledgeBased Systems (IJUFKS
, 1994
"... Abductive inference in Bayesian belief networks is intended as the process of generating the K most probable configurations given an observed evidence. These configurations are called explanations and in most of the approaches found in the literature, all the explanations have the same number of lit ..."
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Cited by 2 (1 self)
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Abductive inference in Bayesian belief networks is intended as the process of generating the K most probable configurations given an observed evidence. These configurations are called explanations and in most of the approaches found in the literature, all the explanations have the same number of literals. In this paper we study how to simplify the explanations in such a way that the resulting configurations are still accounting for the observed facts.