Directed Acyclic Graphs
01 Jan 2014-pp 1481-1517
About: The article was published on 2014-01-01. It has received 17 citations till now. The article focuses on the topics: Directed acyclic graph.
Citations
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01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations
University College London1, University of Oxford2, National Institute for Health Research3, London School of Economics and Political Science4, University of London5, University of Manchester6, Manchester Academic Health Science Centre7, University of Aberdeen8, École nationale d'administration publique9, Urban Institute10, University of Leicester11, University of Sussex12, University of Kent13, University of Leeds14, King's College London15, Ottawa Hospital Research Institute16, University of Warwick17, Cardiff University18, Medical Research Council19, Indiana University20, University of Sheffield21, University of York22, Bangor University23, University of California, Berkeley24, University of Western Ontario25
TL;DR: This essay argues for an evidence synthesis framework that first models the underlying phenomena common across different health-care settings and then makes inferences regarding these phenomena from data.
Abstract: Policies and interventions in the health-care system may have a wide range of effects on multiple patient outcomes and operate through many clinical processes. This presents a challenge for their evaluation, especially when the effect on any one patient is small. In this essay, we explore the nature of the health-care system and discuss how the empirical evidence produced within it relates to the underlying processes governing patient outcomes. We argue for an evidence synthesis framework that first models the underlying phenomena common across different health-care settings and then makes inferences regarding these phenomena from data. Bayesian methods are recommended. We provide the examples of electronic prescribing and increased consultant provision at the weekend.
131 citations
TL;DR: The worsening of SRH with increasing working hours only among women suggests that female workers are more vulnerable to long working hours because of family responsibilities in addition to their workload.
Abstract: Background
To investigate the association between long working hours and self-rated health (SRH), examining the roles of potential confounding and mediating factors, such as job characteristics.
25 citations
TL;DR: Women meeting the physical activity recommendation during pregnancy have a lower risk of PPDS compared to women who are not active during pregnancy, and the results for MVPA persisted in the sub-sample of ethnic minority women.
18 citations
TL;DR: In this article, a Ubersicht with einem Uberblick zur Einfuhrung der Disease Management Programme (DMP), zur Begleitdokumentation, der heutigen Inanspruchnahme sowie the Programmkosten widmet sich der vorliegende Beitrag vor allem der Frage der Nutzenbewertung von DMP.
Abstract: Nach einer Hinleitung zum Thema mit einem Uberblick zur Einfuhrung der Disease-Management-Programme (DMP), zur Begleitdokumentation, der heutigen Inanspruchnahme sowie der Programmkosten widmet sich der vorliegende Beitrag vor allem der Frage der Nutzenbewertung von DMP. Anschliesend an eine Bewertung der gesetzlich vorgegebenen Evaluationsanforderungen werden in Ermangelung einer prospektiven randomisierten kontrollierten Studie (RCT) vor allem die Ergebnisse dreier Studien zusammengefasst, die unter Verwendung eines Propensity-Score-Matchings Routinedaten der gesetzlichen Krankenversicherung zur Kontrollgruppenbildung nutzen. Die Ubersicht endet mit einem Ausblick auf geplante Anderungen in der Evaluation und die beabsichtigte Erganzung der DMP um Programme fur weitere chronische Krankheiten.
14 citations
References
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TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.
41,772 citations
Book•
17 Aug 2006TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
22,840 citations
TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.
Abstract: 1. Introduction to probabilities, graphs, and causal models 2. A theory of inferred causation 3. Causal diagrams and the identification of causal effects 4. Actions, plans, and direct effects 5. Causality and structural models in the social sciences 6. Simpson's paradox, confounding, and collapsibility 7. Structural and counterfactual models 8. Imperfect experiments: bounds and counterfactuals 9. Probability of causation: interpretation and identification Epilogue: the art and science of cause and effect.
12,606 citations
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations
TL;DR: A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented in this paper, where the objective is to specify the benefits of randomization in estimating causal effects of treatments.
Abstract: A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented. The objective is to specify the benefits of randomization in estimating causal effects of treatments. The basic conclusion is that randomization should be employed whenever possible but that the use of carefully controlled nonrandomized data to estimate causal effects is a reasonable and necessary procedure in many cases. Recent psychological and educational literature has included extensive criticism of the use of nonrandomized studies to estimate causal effects of treatments (e.g., Campbell & Erlebacher, 1970). The implication in much of this literature is that only properly randomized experiments can lead to useful estimates of causal effects. If taken as applying to all fields of study, this position is untenable. Since the extensive use of randomized experiments is limited to the last half century,8 and in fact is not used in much scientific investigation today,4 one is led to the conclusion that most scientific "truths" have been established without using randomized experiments. In addition, most of us successfully determine the causal effects of many of our everyday actions, even interpersonal behaviors, without the benefit of randomization. Even if the position that causal effects of treatments can only be well established from randomized experiments is taken as applying only to the social sciences in which
8,377 citations