The Structure Model Interpretation of Wright’s NESS Test

Baldwin, Richard A.; Neufeld, Eric

doi:10.1007/3-540-44886-1_4

Cited by 4 publications

(4 citation statements)

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“…However, a result of Hopkins and Pearl (2003) shows that this is not the case. We illustrate this through re-analysis of the examples in (Baldwin and Neufeld, 2003) and in the process lend validity to Wright's controversial NESS analysis of a complex class of causal scenarios known as double omission cases. We illustrate this through re-analysis of the examples in (Baldwin and Neufeld, 2003) and in the process lend validity to Wright's controversial NESS analysis of a complex class of causal scenarios known as double omission cases.…”

Section: Introductionmentioning

confidence: 83%

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Advances in Artificial Intelligence

Ibrahim¹,

Tawfik²

2004

Lecture Notes in Computer Science

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PrefaceFollowing a long tradition of excellence, the seventeenth edition of the conference of the Canadian Society for the Computational Studies of Intelligence continued the success of its predecessors. This edition reflected the energy and diversity of the Canadian AI community and the many international partnerships that this community has successfully established.AI 2004 attracted high-quality submissions from Canada and around the world. All papers submitted were thoroughly reviewed by the program committee. Each paper was assigned to at least three program committee members. Out of 105 submissions to the main conference, 29 papers were included as full papers in this volume, and 22 as short/position papers. Three workshops and a graduate symposium were also associated with AI 2004. In this volume, 14 papers selected from 21 submissions to the graduate symposium have been included. We invited three distinguished researchers to give talks representing their active research in AI: Fahiem Bacchus, Michael Littman, and Manuela Veloso.It would have been impossible to organize such a successful conference without the help of many individuals. We would like to express our appreciation to the authors of the submitted papers, and to the program committee members and external referees who provided timely and significant reviews. In particular, we would like to thank Luis Rueda for organizing the reviewing of the graduate symposium submissions, and Eric Mulvaney for providing valuable assistance in the preparation of the proceedings. To manage the submission and reviewing process we used CyberChair developed by Richard van de Stadt. Christine Günther from Springer has patiently attended to many editorial details. We owe special thanks to Bob Mercer for handling the local arrangements. Last, but not least, we would like to thank the General Chair, Kay Wiese and all the steering committee members for all their tremendous efforts in making AI 2004 a successful conference.

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Section: Introductionmentioning

confidence: 83%

“…In (Baldwin and Neufeld, 2003) we suggested that the Halpern-Pearl definition essentially formalizes Wright's NESS test. However, a result of Hopkins and Pearl (2003) shows that this is not the case.…”

Section: Introductionmentioning

confidence: 99%

Advances in Artificial Intelligence

Ibrahim¹,

Tawfik²

2004

Lecture Notes in Computer Science

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“…Interestingly,Baldwin and Neufeld [2003] claimed that the NESS test could be formalized using causal models, but did not actually show how, beyond describing some examples. In a later paper[Baldwin and Neufeld 2004], they seem to retract the claim that the NESS test can be formalized using causal models.13 This is a variant of an example originally due toHanson [1958].…”

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confidence: 99%

Cause, responsibility and blame: a structural-model approach

Halpern

2015

Law, Probability and Risk

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A definition of causality introduced by Halpern and Pearl [2005a], which uses structural equations, is reviewed. A more refined definition is then considered, which takes into account issues of normality and typicality, which are well known to affect causal ascriptions. Causality is typically an all-or-nothing notion: either A is a cause of B or it is not. An extension of the definition of causality to capture notions of degree of responsibility and degree of blame, due to Chockler and Halpern [2004], is reviewed. For example, if someone wins an election 11-0, then each person who votes for him is less responsible for the victory than if he had won 6-5. Degree of blame takes into account an agent's epistemic state. Roughly speaking, the degree of blame of A for B is the expected degree of responsibility of A for B, taken over the epistemic state of an agent. Finally, the structural-equations definition of causality is compared to Wright's [It is generally agreed that the notion of legal cause is sorely in need of clarification. Not surprisingly, there has been a great deal of effort in the legal community to provide that clarification (see [Hart and Honoré 1985;Wright 1988] and the references therein). Philosophers have also spent a great deal of effort attempting to clarify causality (see [Collins, Hall, and Paul 2004] for a recent collection of articles on the subject, along with pointers to the literature). Recently there has also been work on causality be computer scientists. It is that work that I report on here. In particular, I describe a definition of causality due to Halpern and Pearl [2005a]; I henceforth call this the HP definition.The HP definition is more formal and mathematical than other definitions of causality in the literature. While this does add some initial overhead, it has the advantage of there being far less ambiguity. There is no need, as in most other definitions, to try to understand how to interpret the English (what counts as a "sufficient condition"? what is a "part" of a sufficient condition?). The first step in the HP definition involves building a formal model in which causality can be determined unambiguously. The definition will then say only that A is a cause of B in model M . It is possible to construct two closely related models M 1 and M 2 such that A is a cause of B in M 1 but not in M 2 . There is not necessarily a "right" model (and, in any case, the definition is silent on what makes one model better than another, although see [Halpern and Hitchcock 2010] for some discussion of this issue). That means that, even if there is agreement regarding the definition of causality, there may be disagreement about which model better describes the real world. I view this as a feature of the definition. It moves the question of actual causality to the right arena-debating which of two (or more) models of the world is a better representation of those aspects of the world that one wishes to capture and reason about. This, indeed, is the type of debate that goes on in informal (and le...

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confidence: 99%

Defaults and Normality in Causal Structures

Halpern

2008

Preprint

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A serious defect with the Halpern-Pearl (HP) definition of causality is repaired by combining a theory of causality with a theory of defaults. In addition, it is shown that (despite a claim to the contrary) a cause according to the HP condition need not be a single conjunct. A definition of causality motivated by Wright's NESS test is shown to always hold for a single conjunct. Moreover, conditions that hold for all the examples considered by HP are given that guarantee that causality according to (this version) of the NESS test is equivalent to the HP definition.

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The Structure Model Interpretation of Wright’s NESS Test

Cited by 4 publications

References 4 publications

Advances in Artificial Intelligence

Advances in Artificial Intelligence

Cause, responsibility and blame: a structural-model approach

Defaults and Normality in Causal Structures

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