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21
Foundations for Bayesian networks
, 2001
"... Bayesian networks are normally given one of two types of foundations: they are either treated purely formally as an abstract way of representing probability functions, or they are interpreted, with some causal interpretation given to the graph in a network and some standard interpretation of probabi ..."
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Cited by 11 (7 self)
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Bayesian networks are normally given one of two types of foundations: they are either treated purely formally as an abstract way of representing probability functions, or they are interpreted, with some causal interpretation given to the graph in a network and some standard interpretation of probability given to the probabilities specified in the network. In this chapter I argue that current foundations are problematic, and put forward new foundations which involve aspects of both the interpreted and the formal approaches. One standard approach is to interpret a Bayesian network objectively: the graph in a Bayesian network represents causality in the world and the specified probabilities are objective, empirical probabilities. Such an interpretation founders when the Bayesian network independence assumption (often called the causal Markov condition) fails to hold. In §2 I catalogue the occasions when the independence assumption fails, and show that such failures are pervasive. Next, in §3, I show that even where the independence assumption does hold objectively, an agent’s causal knowledge is unlikely to satisfy the assumption with respect to her subjective probabilities, and that slight differences between an agent’s subjective Bayesian network and an objective Bayesian network can lead to large differences between probability distributions determined by these networks. To overcome these difficulties I put forward logical Bayesian foundations in §5. I show that if the graph and probability specification in a Bayesian network are thought of as an agent’s background knowledge, then the agent is most rational if she adopts the probability distribution determined by the
Causal Inference By Choosing Graphs With Most Plausible Markov Kernels
 MARKOV KERNELS, NINTH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL INTELLIGENCE AND MATHEMATICS
, 2006
"... We propose a new inference rule for estimating causal structure that underlies the observed statistical dependencies among n random variables. Our method is based on comparing the conditional distributions of variables given their direct causes (the socalled "Markov kernels") for all h ..."
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Cited by 11 (10 self)
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We propose a new inference rule for estimating causal structure that underlies the observed statistical dependencies among n random variables. Our method is based on comparing the conditional distributions of variables given their direct causes (the socalled "Markov kernels") for all hypothetical causal directions and choosing the most plausible one. We consider those Markov kernels most plausible, which maximize the (conditional) entropies constrained by their observed first moment (expectation) and second moments (variance and covariance with its direct causes) based on their given domain. In this
Causation, Statistics, and Sociology
 EUROPEAN SOCIOLOGICAL REVIEW,VOL. 17 NO. 1, 1^20
, 2001
"... Three different understandings of causation, each importantly shaped by the work of statisticians, are examined from the point of view of their value to sociologists: causation as robust dependence, causation as consequential manipulation, and causation as generative process. The last is favoured as ..."
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Cited by 8 (0 self)
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Three different understandings of causation, each importantly shaped by the work of statisticians, are examined from the point of view of their value to sociologists: causation as robust dependence, causation as consequential manipulation, and causation as generative process. The last is favoured as the basis for causal analysis in sociology. It allows the respective roles of statistics and theory to be clarified and is appropriate to sociology as a largely nonexperimental social science in which the concept of action is central.
Generic versus singlecase causality: the case of autopsy. European Journal for Philosophy of Science, forthcoming
, 2011
"... This paper addresses questions about how the levels of causality (generic and singlecase causality) are related. One question is epistemological: can relationships at one level be evidence for relationships at the other level? We present three kinds of answer to this question, categorised according ..."
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Cited by 7 (5 self)
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This paper addresses questions about how the levels of causality (generic and singlecase causality) are related. One question is epistemological: can relationships at one level be evidence for relationships at the other level? We present three kinds of answer to this question, categorised according to whether inference is topdown, bottomup, or the levels are independent. A second question is metaphysical: can relationships at one level be reduced to relationships at the other level? We present three kinds of answer to this second question, categorised according to whether singlecase relations are reduced to generic, generic relations are reduced to singlecase, or the levels are independent. We then explore causal inference in autopsy. This is an interesting case study, we argue, because it refutes all three epistemologies and all three metaphysics. We close by sketching an account of causality that
Machine Learning and the Philosophy of Science: a Dynamic Interaction
 In Proceedings of the ECMLPKDD01 Workshop on Machine Leaning as Experimental Philosophy of Science
, 2001
"... I posit here a dynamic interaction between machine learning and the philosophy of science, and illustrate this claim with the use of a case study involving the foundations of Bayesian networks. ..."
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Cited by 4 (1 self)
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I posit here a dynamic interaction between machine learning and the philosophy of science, and illustrate this claim with the use of a case study involving the foundations of Bayesian networks.
Error Explanation and Fault Localization with Distance Metrics
, 2005
"... contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the sponsoring institutions, the U.S. Government or any other entity. ..."
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Cited by 4 (0 self)
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contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the sponsoring institutions, the U.S. Government or any other entity.
A dynamic interaction between machine learning and the philosophy of science
 Minds and Machines
, 2004
"... The relationship between machine learning and the philosophy of science can be classed as a dynamic interaction: a mutually beneficial connection between two autonomous fields that changes direction over time. I discuss the nature of this interaction and give a case study highlighting interactions b ..."
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Cited by 3 (1 self)
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The relationship between machine learning and the philosophy of science can be classed as a dynamic interaction: a mutually beneficial connection between two autonomous fields that changes direction over time. I discuss the nature of this interaction and give a case study highlighting interactions between research on Bayesian networks in machine learning and research on causality and probability in the philosophy of science.
A Criterion of Probabilistic Causality
"... The investigation of probabilistic causality has been plagued by a variety of misconceptions and misunderstandings. One has been the thought that the aim of the probabilistic account of causality is the reduction of causal claims to probabilistic claims. Nancy Cartwright (1979) has clearly rebut ..."
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Cited by 1 (1 self)
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The investigation of probabilistic causality has been plagued by a variety of misconceptions and misunderstandings. One has been the thought that the aim of the probabilistic account of causality is the reduction of causal claims to probabilistic claims. Nancy Cartwright (1979) has clearly rebutted that idea. Another illconceived idea continues to haunt the debate, namely the idea that contextual unanimity can do the work of objective homogeneity. It cannot. We argue that only objective homogeneity in combination with a causal interpretation of Bayesian networks can provide the desired criterion of probabilistic causality.
§3 The ComplexSystems Theory §4 General Problems for Mechanistic Causality
, 2011
"... After introducing a range of mechanistic theories of causality and some of the problems they face, I argue that while there is a decisive case against a purely mechanistic analysis, a viable theory of causality must incorporate mechanisms as an ingredient. I describe one way of providing an analysis ..."
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After introducing a range of mechanistic theories of causality and some of the problems they face, I argue that while there is a decisive case against a purely mechanistic analysis, a viable theory of causality must incorporate mechanisms as an ingredient. I describe one way of providing an analysis of causality which reaps the rewards of the mechanistic approach