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Fusion, Propagation, and Structuring in Belief Networks
, 1986
"... Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used to repre ..."
Abstract

Cited by 381 (7 self)
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Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used to represent the generic knowledge of a domain expert, and it turns into a computational architecture if the links are used not merely for storing factual knowledge but also for directing and activating the data flow in the computations which manipulate this knowledge. The first part of the paper deals with the task of fusing and propagating the impacts of new information through the networks in such a way that, when equilibrium is reached, each proposition will be assigned a measure of belief consistent with the axioms of probability theory. It is shown that if the network is singly connected (e.g. treestructured), then probabilities can be updated by local propagation in an isomorphic network of parallel and autonomous processors and that the impact of new information can be imparted to all propositions in time proportional to the longest path in the network. The second part of the paper deals with the problem of finding a treestructured representation for a collection of probabilistically coupled propositions using auxiliary (dummy) variables, colloquially called "hidden causes. " It is shown that if such a treestructured representation exists, then it is possible to uniquely uncover the topology of the tree by observing pairwise dependencies among the available propositions (i.e., the leaves of the tree). The entire tree structure, including the strengths of all internal relationships, can be reconstructed in time proportional to n log n, where n is the number of leaves.
Fundamental Concepts of Qualitative Probabilistic Networks
 ARTIFICIAL INTELLIGENCE
, 1990
"... Graphical representations for probabilistic relationships have recently received considerable attention in A1. Qualitative probabilistic networks abstract from the usual numeric representations by encoding only qualitative relationships, which are inequality constraints on the joint probability dist ..."
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Cited by 119 (6 self)
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Graphical representations for probabilistic relationships have recently received considerable attention in A1. Qualitative probabilistic networks abstract from the usual numeric representations by encoding only qualitative relationships, which are inequality constraints on the joint probability distribution over the variables. Although these constraints are insufficient to determine probabilities uniquely, they are designed to justify the deduction of a class of relative likelihood conclusions that imply useful decisionmaking properties. Two types of qualitative relationship are defined, each a probabilistic form of monotonicity constraint over a group of variables. Qualitative influences describe the direction of the relationship between two variables. Qualitative synergies describe interactions among influences. The probabilistic definitions chosen justify sound and efficient inference procedures based on graphical manipulations of the network. These procedures answer queries about qualitative relationships among variables separated in the network and determine structural properties of optimal assignments to decision variables.
Causation As A Secondary Quality
, 1993
"... Introduction. 2. The agency theory: a probabilistic version outlined. 3. Metaphysics confused with epistemology? 4. Unavoidable circularity? 5. Unmanipulable causes? 6. Unacceptable anthropocentricity? 1 INTRODUCTION In this paper we defend the view that the ordinary notions of cause and effect ..."
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Cited by 16 (5 self)
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Introduction. 2. The agency theory: a probabilistic version outlined. 3. Metaphysics confused with epistemology? 4. Unavoidable circularity? 5. Unmanipulable causes? 6. Unacceptable anthropocentricity? 1 INTRODUCTION In this paper we defend the view that the ordinary notions of cause and effect have a direct and essential connection with our ability to intervene in the world as agents. 1 This is a well known but rather unpopular philosophical approach to causation, often called the manipulability theory. In the interests of brevity and accuracy, we prefer to call it the agency theory. 2 Thus the central thesis of an agency account of causation is something like this: an event A is a cause of a distinct event B just in case bringing about the occurrence of A would be an effective means by which a free agent could bring about the occurrence o
Latent variables, causal models and overidentifying constraints
 Journal of Econometrics
, 1988
"... When is a statistical dependency between two variables best explained by the supposition that one of these variables causes the other, as opposed to the supposition that there is a (possibly unmeasured) common cause acting on both variables? In this paper, we describe an approach towards model speci ..."
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Cited by 7 (0 self)
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When is a statistical dependency between two variables best explained by the supposition that one of these variables causes the other, as opposed to the supposition that there is a (possibly unmeasured) common cause acting on both variables? In this paper, we describe an approach towards model specification developed more fully in our book Discovering Cuud Structure, and illustrate its application to the aforementioned question. Briefly, the approach is to determine constraints satisfied by the variancecovariance matrix of a sample, and then to conduct a quasiautomated search for the causal specifications that will best explain those constraints, 1.
Systems biology via redescription and ontologies (II): A Tool for Discovery in Complex Systems
"... A complex system creates a “whole that is larger than the sum of its parts,” by coordinating many interacting simpler component processes. Yet, each of these processes is difficult to decipher as their visible signatures are only seen in a syntactic background, devoid of the context. Examples of suc ..."
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Cited by 7 (3 self)
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A complex system creates a “whole that is larger than the sum of its parts,” by coordinating many interacting simpler component processes. Yet, each of these processes is difficult to decipher as their visible signatures are only seen in a syntactic background, devoid of the context. Examples of such visible datasets are timecourse description of geneexpression abundance levels, neural spiketrains, or clickstreams for web pages. It has now become rather effortless to collect voluminous datasets of this nature; but how can we make sense of them and draw significant conclusions? For instance, in the case of timecourse geneexpression datasets, rather than following small sets of known genes, can we develop a holistic approach that provides a view of the entire system as it evolves through time? We have developed GOALIE (GeneOntology for Algorithmic Logic and Invariant Extraction) a systems biology application that presents global and dynamic perspectives (e.g., invariants) inferred collectively over a geneexpression dataset. Such perspectives are important in order to obtain a processlevel understanding of the underlying cellular machinery; especially how cells react, respond, and recover from
Causality in the Social and Behavioral Sciences
 A PAPER SUBMITTED TO SOCIOLOGICAL METHODOLOGY.
, 2009
"... This paper aims to acquaint researchers in the quantitative social and behavior sciences with recent advances in causal inference which provide a systematic methodology for defining, estimating, testing, and defending causal claims in experimental and observational studies. These advances are illust ..."
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Cited by 1 (1 self)
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This paper aims to acquaint researchers in the quantitative social and behavior sciences with recent advances in causal inference which provide a systematic methodology for defining, estimating, testing, and defending causal claims in experimental and observational studies. These advances are illustrated using a general theory of causation based on nonparametric structural equation models (SEM) – a natural generalization of those used by econometricians and social scientists in the 195060s, which provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called “causal effects” or “policy evaluation”) (2) queries about probabilities of counterfactuals, (including assessment of “regret,” “attribution” or “causes of effects”) and (3) queries about direct and indirect effects (also known as “mediation”). Finally, the paper clarifies the role of propensity score matching in causal analysis, defines the relationships between the structural and potentialoutcome frameworks, and develops symbiotic tools that use the strong features of both.
A Formal Logical Analysis of Causal Relations Summary
"... Causal relations of various kinds are a pervasive feature of human language and theorising about the world. Despite this, the specification of a satisfactory general analysis of causal relations has long proved difficult. The research described in this thesis is an attempt to provide a formal logica ..."
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Causal relations of various kinds are a pervasive feature of human language and theorising about the world. Despite this, the specification of a satisfactory general analysis of causal relations has long proved difficult. The research described in this thesis is an attempt to provide a formal logical theory of causal relations, in a broad sense of ‘causal’, which includes various atemporal explanatory and functional relations, in addition to causation between temporally ordered events; and which involves not only necessity associated with physical laws, but also necessity associated with laws and constraints of various other types. The key idea which motivates the analysis is that many types of causal relation have in common certain underlying abstract properties, regardless of the nature of the participants involved. These properties can be expressed via an axiomatisation, initially viewed as applicable to ‘event causation’, but subsequently reinterpreted in a more abstract and general way. Given the wide variety of models for the axioms, there are not likely to be powerful general methods for computing the causal relationships defined: instead it is likely to
ARTIFICIAL INTELLIGENCE 241 Fusion, Propagation, and Structuring in Belief Networks*
"... Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used to repre ..."
Abstract
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Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used to represent the generic knowledge of a domain expert, and it turns into a computational architecture if the links are used not merely for storing factual knowledge but also for directing and activating the data flow in the computations which manipulate this knowledge. The first part of the paper deals with the task of fusing and propagating the impacts of new information through the networks in such a way that, when equilibrium is reached, each proposition will be assigned a measure of belief consistent with the axioms of probability theory. It is shown that if the network is singly connected (e.g. treestructured), then probabilities can be updated by local propagation in an isomorphic network of parallel and autonomous processors and that the impact of new information can be imparted to all propositions in time proportional to the longest path in the network. The second part of the paper deals with the problem of finding a treestructured representation for a collection of probabilistically coupled propositions using auxiliary (dummy) variables, colloquially called "hidden causes. " It is shown that if such a treestructured representation exists, then it is possible to uniquely uncover the topology of the tree by observing pairwise dependencies among the available propositions (i.e., the leaves of the tree). The entire tree structure, including the strengths of all internal relationships, can be reconstructed in time proportional to n log n, where n is the number of leaves. 1.
A Tool for Discovery in Complex Systems
"... A complex system creates a “whole that is larger than the sum of its parts,” by coordinating many interacting simpler component processes. Yet, each of these processes is difficult to decipher as their visible signatures are only seen in a syntactic background, devoid of the context. Examples of suc ..."
Abstract
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A complex system creates a “whole that is larger than the sum of its parts,” by coordinating many interacting simpler component processes. Yet, each of these processes is difficult to decipher as their visible signatures are only seen in a syntactic background, devoid of the context. Examples of such visible datasets are timecourse description of geneexpression abundance levels, neural spiketrains, or clickstreams for web pages. It has now become rather effortless to collect voluminous datasets of this nature; but how can we make sense of them and draw significant conclusions? For instance, in the case of timecourse geneexpression datasets, rather than following small sets of known genes, can we develop a holistic approach that provides a view of the entire system as it evolves through time? We have developed GOALIE (GeneOntology for Algorithmic Logic and Invariant Extraction) a systems biology application that presents global and dynamic perspectives (e.g., invariants) inferred collectively over a geneexpression dataset. Such perspectives are important in order to obtain a processlevel understanding of the underlying cellular machinery; especially how cells react, respond, and recover from
BIOSCIENCE COMPUTING Metamorphosis: the Coming Transformation of Translational Systems Biology
"... In the future computers will mine patient data to deliver faster, cheaper healthcare, but how will we design them to give informative causal explanations? Ideas from philosophy, model checking, and statistical testing can pave the way for the needed translational systems biology. Samantha Kleinberg ..."
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In the future computers will mine patient data to deliver faster, cheaper healthcare, but how will we design them to give informative causal explanations? Ideas from philosophy, model checking, and statistical testing can pave the way for the needed translational systems biology. Samantha Kleinberg and Bud Mishra, New York University One morning, as Gregorina Samsa was waking up from anxious dreams, she discovered that she had become afflicted with certain mysterious flulike symptoms that appeared without any warning. Equally irritating, this capricious metamorphosis seemed impervious to a rational explanation in terms of causes and effects. “What’s happened to me? ” she thought. Before seeing a doctor, she decided to find out more about what might ail her. She logged on to a Web site where she annotated a timeline with what she could remember. Since March, she’d had more headaches than usual, and then in April she had begun to experience more fatigue after exercise, and as of July she had also experienced occasional lapses in memory. “Why don’t I go back to sleep for a little while longer and forget all this foolishness, ” she thought. As she was about to abandon this errand, the system came back to life with a barrage of questions: Is she female? Had she experienced any significant stress in the past few months? Had she noticed any joint or muscle pain? It also obtained her permission to