Dissertations / Theses on the topic 'Causality'
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Al, Sadoon Trujillo Majid. "Causality along subspaces." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609157.
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Jatta, Abdullah. "Test of Causality in Conditional Variance Hafner and Herwatz Test for Causality." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-47701.
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Wachter, Daniel von. "Modality, causality, and God." Thesis, University of Oxford, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289017.
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Murphy, David V. J. "Time, causality, and concurrency." Thesis, University of Surrey, 1989. http://epubs.surrey.ac.uk/976/.
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Cattle, Kirsty. "Faecal incontinence : obstetric causality." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/faecal-incontinence-obstetric-causality(c98b4d67-566b-4e5c-b17b-6546387d30ea).html.
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Introduction: Faecal incontinence is more common in parous women who have had a difficult vaginal delivery. However, the pathophysiology of the injury resulting in faecal incontinence in such women is incompletely understood. This study therefore aimed to compare anal canal and pelvic floor parameters between continent and incontinent women and measure these during pregnancy and after delivery in order to more fully understand the initial insult to the pelvic floor. Methods: Anal manometry and fatigue (using a water-filled microballoon) and pelvic floor strength and fatigue (using an air-filled vaginal probe connected to a Peritron) were measured in 30 primiparous women at booking, end of pregnancy and 6 months post partum. Ten of these women also underwent measurement of pelvis size using ultrasound. A further 61 women, 39 incontinent and 22 continent, also underwent these measurements in order to compare pelvic floor parameters between continent and incontinent women. Results: Voluntary contraction of the external anal sphincter (EAS) was significantly lower 11 weeks post partum than antenatal values (106.5 ± 43.6 cmH2O antenatally vs 75.5 ± 45.6 cmH2O post partum, p < 0.001) but there was no significant difference between antenatal values and those measured 6 months post partum (p = 0.24). Anal fatigue rate was significantly slower 11 weeks post partum (p = 0.001), but by six months post partum the difference is no longer significant (p = 0.053). Pelvic floor muscle (PFM) strength fell with age and was significantly lower in incontinent women (8.97 ± 12.88 cmH2O) than incontinent women (27.17 ± 18.16 cmH2O; p < 0.001). PFM fatigue rate was also significantly slower in incontinent women (p = 0.01). The PFM strength was significantly higher in nulliparous than parous women (p = 0.002) and fatigue rate was faster (p = 0.022). PFM strength (p = 0.006) and fatigue rate (p =0.004) were significantly lower six months post partum when compared with antenatal values. It was shown that pelvis size can be measured using ultrasound and was found to be repeatable, but inaccurate when compared with magnetic resonance imaging. Insufficient numbers were studied to show an effect on pelvic floor function. Conclusion: Vaginal delivery causes impairment of EAS voluntary contraction which appears to have recovered by six months post partum. It also causes impairment of PFM contraction which is persistent at six months post partum. The reduced PFM function seen post partum also occurs in incontinent women, adding to the evidence that childbirth causes the initial insult to the pelvic floor which results in faecal incontinence, either immediately or some years later.
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AZEVEDO, RONALDO. "GRANGER CAUSALITY IN TIME SERIES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1991. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8782@1.
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REDE FERROVIÁRIA FEDERAL SANeste trabalho fazemos uma revisita à causalidade no sentido de Granger aplicada à s Séries Temporais bivariadas no domÃnio do tempo e da freqüência. Um programa computacional foi escrito usando a linguagem Pascal para, testando casos reais e simulados, construir modelos de causalidade/feedback, que são então analisados no ambiente espectral, com ênfase maior à discussão da coerência e da fase de causalidade.
In this work causality in the sense defined by Granger is revisited. Applications to bivariante temporal systems in time domain and frequency-domain were analysed, using a computer program written in Pascal. After this, spectral methods were developed, with special emphasis on phase and causality-coerence.
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Larkin, Mark John William. "Retrospective revaluation in causality judgement." Thesis, University of Cambridge, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.624120.
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McKay, A. C. "Causality in a McDowellian world." Thesis, Queen's University Belfast, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.679262.
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The thesis explores and suggests a solution to a problem that I identify in John McDowell's and Lynne Rudder Baker's approaches to mental and intention-dependent (ID) causation in the physical world. I discuss McDowell's non-reductive and anti-scientistic account of mind and world, which I believe promises to renew and liberate philosophy. Baker's constitution account, I argue, provides a potential link between McDowell's categories of the space of reasons and the realm of law. However, both McDowell and Baker view mental causation as acting unproblematically within the physical world. I argue that this is inconsistent with an understanding of ordinary physical causality as objective, in the sense of being recognition-independent, and as causally complete. I develop an account of what I call manifest physical causation - of objective causal relations in the ordinary world of Wilfrid Sellars's manifest image and its extensions into the special sciences. Manifest physical causation, on my account, is productive, acts through physical mechanisms, and is causally closed. In my view, mental and ID property-instances are not part of the manifest physical causal nexus. I conclude by suggesting a modification of Baker's constitution account, which I call Constituted Causation, whereby higher-level - mental and other ID - causal relations are constituted, in favourable circumstances, by lower-level ones. Causality, I argue, relates property-instances at the same ontological level. ID causal relations belong in their own causal nexus of rational and normative relations, connected to the manifest physical world through constitution, a relation of unity without identity. On Baker's view, the essential properties of constituted entities subsume those of their constituters. Extending this to my account enables us to say that the real cause and explanation of someone's action is that they consciously performed it rather than that certain causal processes occurred at the lower level.
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Centorrino, Samuele. "Causality, endogeneity and nonparametric estimation." Thesis, Toulouse 1, 2013. http://www.theses.fr/2013TOU10020/document.
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Cette thèse porte sur les problèmes de causalité et d'endogénéité avec estimation non-paramétrique de la fonction d’intérêt. On explore ces problèmes dans deux modèles différents. Dans le cas de données en coupe transversale et iid, on considère l'estimation d'un modèle additif séparable, dans lequel la fonction de régression dépend d'une variable endogène. L'endogénéité est définie, dans ce cas, de manière très générale : elle peut être liée à une causalité inverse (la variable dépendante peut aussi intervenir dans la réalisation des régresseurs), ou à la simultanéité (les résidus contiennent de l'information qui peut influencer la variable indépendante). L'identification et l'estimation de la fonction de régression se font par variables instrumentales. Dans le cas de séries temporelles, on étudie les effets de l'hypothèse d'exogénéité dans un modèle de régression en temps continu. Dans un tel modèle, la variable d'état est fonction de son passé, mais aussi du passé d'autres variables et on s'intéresse à l'estimation nonparamétrique de la moyenne et de la variance conditionnelle. Le premier chapitre traite de ce dernier cas. En particulier, on donne des conditions suffisantes pour qu'on puisse faire de l'inférence statistique dans un tel modèle. On montre que la non-causalité est une condition suffisante pour l'exogénéité, quand on ne veut pas faire d'hypothèses sur les dynamiques du processus des covariables. Cependant, si on est prêt à supposer que le processus des covariables suit une simple équation différentielle stochastique, l'hypothèse de non-causalité devient immatérielle. Les chapitres de deux à quatre se concentrent sur le modèle iid simple. Etant donné que la fonction de régression est solution d'un problème mal-posé, on s'intéresse aux méthodes d'estimation par régularisation. Dans le deuxième chapitre, on considère ce modèle dans le cas d'un régularisation sur la norme L2 de la fonction (régularisation de type Tikhonov). On dérive les propriétés d'un critère de validation croisée pour définir le choix du paramètre de régularisation. Dans le chapitre trois, coécrit avec Jean-Pierre Florens, on étend ce modèle au cas où la variable dépendante n'est pas directement observée mais où on observe seulement une transformation binaire de cette dernière. On montre que le modèle peut être identifié en utilisant la décomposition de la variable dépendante dans l'espace des variables instrumentales et en supposant que les résidus de ce modèle réduit ont une distribution connue. On démontre alors, sous ces hypothèses, qu'on préserve les propriétés de convergence de l'estimateur non-paramétrique. Enfin, le chapitre quatre, coécrit avec Frédérique Fève et Jean-Pierre Florens, décrit une étude numérique, qui compare les propriétés de diverses méthodes de régularisation. En particulier, on discute des critères pour le choix adaptatif des paramètres de lissage et de régularisation et on teste la validité du bootstrap sauvage dans le cas des modèles de régression non-paramétrique avec variables instrumentalesThis thesis deals with the broad problem of causality and endogeneity in econometrics when the function of interest is estimated nonparametrically. It explores this problem in two separate frameworks. In the cross sectional, iid setting, it considers the estimation of a nonlinear additively separable model, in which the regression function depends on an endogenous explanatory variable. Endogeneity is, in this case, broadly denned. It can relate to reverse causality (the dependent variable can also affects the independent regressor) or to simultaneity (the error term contains information that can be related to the explanatory variable). Identification and estimation of the regression function is performed using the method of instrumental variables. In the time series context, it studies the implications of the assumption of exogeneity in a regression type model in continuous time. In this model, the state variable depends on its past values, but also on some external covariates and the researcher is interested in the nonparametric estimation of both the conditional mean and the conditional variance functions. This first chapter deals with the latter topic. In particular, we give sufficient conditions under which the researcher can make meaningful inference in such a model. It shows that noncausality is a sufficient condition for exogeneity if the researcher is not willing to make any assumption on the dynamics of the covariate process. However, if the researcher is willing to assume that the covariate process follows a simple stochastic differential equation, then the assumption of noncausality becomes irrelevant. Chapters two to four are instead completely devoted to the simple iid model. The function of interest is known to be the solution of an inverse problem. In the second chapter, this estimation problem is considered when the regularization is achieved using a penalization on the L2-norm of the function of interest (so-called Tikhonov regularization). We derive the properties of a leave-one-out cross validation criterion in order to choose the regularization parameter. In the third chapter, coauthored with Jean-Pierre Florens, we extend this model to the case in which the dependent variable is not directly observed, but only a binary transformation of it. We show that identification can be obtained via the decomposition of the dependent variable on the space spanned by the instruments, when the residuals in this reduced form model are taken to have a known distribution. We finally show that, under these assumptions, the consistency properties of the estimator are preserved. Finally, chapter four, coauthored with Frédérique Fève and Jean-Pierre Florens, performs a numerical study, in which the properties of several regularization techniques are investigated. In particular, we gather data-driven techniques for the sequential choice of the smoothing and the regularization parameters and we assess the validity of wild bootstrap in nonparametric instrumental regressions
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Allen, John-Mark. "Reality, causality, and quantum theory." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:01413eef-0944-4ec5-ad53-ac8378bcf4be.
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Quantum theory describes our universe incredibly successfully. To our classically-inclined brains, however, it is a bizarre description that requires a reimagining of what fundamental reality, or 'ontology', could look like. This thesis examines different ontological features in light of the success of quantum theory, what it requires, and what it rules out. While these investigations are primarily foundational, they also have relevance to quantum information, quantum communication, and experiments on quantum systems. The way that quantum theory describes the state of a system is one of its most unintuitive features. It is natural, therefore, to ask whether a similarly strange description of states is required on an ontological level. This thesis proves that almost all quantum superposition states for d > 3 dimensions must be real - that is, present in the ontology in a well-defined sense. This is a strong requirement which prevents intuitive explanations of the many quantum phenomena which are based on superpositions. A new theorem is also presented showing that quantum theory is incompatible with macro-realist ontologies, where certain physical quantities must always have definite values. This improves on the Leggett-Garg argument, which also aims to prove incompatibility with macro-realism but contains loopholes. Variations on both of these results that are error-tolerant (and therefore amenable to experimentation) are presented, as well as numerous related theorems showing that the ontology of quantum states must be somewhat similar to the quantum states themselves in various specific ways. Extending these same methods to quantum communication, a simple proof is found showing that an exponential number of classical bits are required to communicate a linear number of qubits. That is, classical systems are exponentially bad at storing quantum data. Causal influences are another part of ontology where quantum theory demands a revision of our classical notions. This follows from the outcomes of Bell experiments, as rigorously shown in recent analyses. Here, the task of constructing a native quantum framework for reasoning about causal influences is tackled. This is done by first analysing the simple example of a common cause, from which a quantum version of Reichenbach's principle is identified. This quantum principle relies on an identification of quantum conditional independence which can be defined in four ways, each naturally generalising a corresponding definition for classical conditional independence. Not only does this allow one to reason about common causes in a quantum experiments, but it can also be generalised to a full framework of quantum causal models (mirroring how classical causal models generalise Reichenbach's principle). This new definition of quantum causal models is illustrated by examples and strengthened by it's foundation on a robust quantum Reichenbach's principle. An unusual, but surprisingly fruitful, setting for considering quantum ontology is found by considering time travel to the past. This provides a testbed for different ontological concepts in quantum theory and new ways to compare classical and quantum frameworks. It is especially useful for comparing computational properties. In particular, time travel introduces non-linearity to quantum theory, which brings (sometimes implicit) ontological assumptions to the fore while introducing strange new abilities. Here, a model for quantum time travel is presented which arguably has fewer objectionable features than previous attempts, while remaining similarly well-motivated. This model is discussed and compared with previous quantum models, as well as with the classical case. Together, these threads of investigation develop a better understanding of how quantum theory affects possible ontologies and how ontological prejudices influence quantum theory.
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Sinfield, James Lister. "Synchronization and causality in biological networks." Thesis, University of Warwick, 2009. http://wrap.warwick.ac.uk/3789/.
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Ng, Suk-fun, and 伍淑芬. "Time and causality in Yogācāra Buddhism." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206667.
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The research explores the interplay between causality and the notion of time in Yogācāra Buddhism. There has been a long debate over whether time is an objective reality with independent ontological status or, in contrast, a subjective experience that is dependent on mind. Until now, the two sides have failed to provide a clear and complete explanation of our temporal conception of things. A similar situation can be identified in the development of the notion of time in Indian philosophy. The concept of time (kāla) in the Indian tradition has evolved from cosmological speculations and the notion of divine power as developed in the Upanisads, where time is identified with Brahman (God), which is postulated as the ultimate ground of existence. On the other hand, in Buddhist philosophy our temporal conception of things is explained with our psychological experience. The limited investigation into the teachings of Yogācāra Buddhism has created a vacuum in our knowledge of the concept of time as understood by this particular Buddhist tradition. The thesis argues that concepts of time in Yogācāra are closely linked with its spiritual practice and its explanation for temporal experience as it occurs in the internal mind. It is the Vijñānavāda theory of causality that mediates between mind and spiritual practice. Here, time is defined as a nominal designation for an uninterrupted series of causal activities. When causality links with the flowing stream of time in the past, present and future, it creates the impression of a linear relation between the cause and the arising of the effect. In this thesis, primary sources in Sanskrit, Tibetan and Chinese are presented in order to show that there are doctrinal materials to support that it is around this central theme on which Yogācāra discussion on time hinger. The thesis demonstrates that the study of time in Yogācāra is divided into three strata: staring from the soteriological investigation by Maitreya and Asanga then developed into phenomenological inquiry in Vasubandhu’s idealistic position, and completed in the epistemological system of Dignāga and Dharmakīrti. This research is intended to fill a gap in the study of the Buddhist concept of time and to provide a possible resolution to the contemporary debate over the nature of temporal notions by examining it from the religious and philosophical perspectives found in Yogācāra Buddhism.published_or_final_version
Buddhist Studies
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Dolbear, Catherine. "Personalised information filtering using event causality." Thesis, University of Oxford, 2004. http://ora.ox.ac.uk/objects/uuid:31e94de4-5dda-4312-968b-d0ef34dea8e2.
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Previous research on multimedia information filtering has mainly concentrated on key frame identification and video skim generation for browsing purposes, however applications requiring the generation of summaries as the final product for user con- sumption are of equal scientific and commercial interest. Recent advances in computer vision have enabled the extraction of semantic events from an audio-visual signal, so it can be assumed for our purposes that such semantic labels are already available for use. We concentrate instead on developing methods to prioritise these semantic elements for inclusion in a summary which can be personalised to meet a particular user's needs. Our work differentiates itself from that in the literature as it is driven by the results of a knowledge elicitation study with expert summarisers. The experts in our study believe that summaries structured as a narrative are better able to convey the content of the original data to a user. Motivated by the information filtering problem, the primary contribution of this thesis is the design and implementation of a system to summarise sequences of events by automatic modelling of the causal relationships between them. We show, by com- parison against summaries generated by experts and with the introduction of a new coherence metric, that modelling the causal relationships between events increases the coherence and accuracy of summaries. We suggest that this claim is valid, not only in the domain of soccer highlights generation, in which we carry out the bulk of our experiments, but also in any other domain in which causal relationships can be iden- tified between events. This proposal is tested by applying our summarisation system to another, significantly different domain, that of business meeting summarisation, using the soccer training set and a manually generated ontology mapping. We introduce the concept of a context-group of causally related events as a first step towards modelling narrative episodes and present a comparison between a case based reasoning and a two-stage Markov model approach to summarisation. For both methods we show that by including entire context-groups in the summary, rather than single events in isolation, more accurate summaries can be generated. Our approach to personalisation biases a summary according to particular narrative plotlines using different subsets of the training data. Results show that the number of instances of certain event classes can be increased by biasing the training set appropriately. This method gives very similar results to a standard weighting method, while avoiding the need to tailor the weights to a particular application domain.
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Mills, S. "Gilbert Simondon : causality, ontogenesis & technology." Thesis, University of the West of England, Bristol, 2014. http://eprints.uwe.ac.uk/22786/.
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This PhD thesis focuses on the elucidation, development and application of Gilbert Simondon's realist philosophy of individuation. In particular the thesis has three main goals: First, to provide a developed account of Simondon's ontology. Second, to develop a coherent account of causality in line with Simondon's theorization of individuation. Third, to give a full account of Simondon's philosophy of technology and evaluate its relevance for the contemporary technological state of affairs. To answer the third of these questions it is necessary to address the others. A realist, non-anthropological account of technology necessarily requires the development of a robust ontology and a suitable theorization of causality. In this thesis this is achieved by developing the key concepts involved in Simondon's theory of individuation such as transduction, metastability and pre-individuality. Before developing an account of transductive operation in the three regimes of individuation which Simondon stipulates (physical, vital and psycho-social) we argue for Simondon's account of allagmatics (theory of operations) as consistent with and in some ways superior to some contemporary powers based theories of causality. Having established the broad scope of Simondon's axiomatic use of individuation it is then utilized in order to fully examine his philosophy of technology. This is achieved by bringing together Simondon's theorization of individuation in multiple domains (e.g. the image-cycle, transindividual) in relation to that of technology. In doing this we also develop other important aspects of Simondon's philosophy such as aesthetics, epistemology and ethics. By necessity the thesis has a broad scope in order to reflect the encyclopedic ambition which Simondon had for his genetic philosophy and without which his work is prone to be misunderstood. As such it describes a novel encounter between cybernetics, phenomenology and energetics.
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Scott, Thomas Petrie. "Dual causality and Bell's essential conflict." Thesis, University of Aberdeen, 2014. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=225331.
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The Bible claims that God actively governs the world on a day-to-day basis; that is, he is an active causal agent. The Bible also appears to claim that there are naturally occurring events; that is, in addition to divine causality there is natural causality. The thesis explores whether dual causality is theologically viable; whether nature may allow space for God to govern without violation of the laws of nature; and the desirability of causal closure. Having arrived at the conclusion that dual causality is viable both theologically and physically, and that causal closure is desirable, it will be proposed that dual causality is a feature of the world. Arguably the most notable dispute in twentieth century physics was that between Niels Bohr and Albert Einstein, culminating in the EPR thought experiment. The EPR paradox was refined and clarified by John Bell into an 'essential conflict' between special relativity and any interpretation of quantum mechanics. As a test case dual causality will be applied to Bell's essential conflict. The thesis claims that dual causality resolves Bell's essential conflict and also provides the most complete explanation of the world as revealed by modern physics. It will suggest that a theological interpretation of quantum mechanics may be possible.
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Chetlur, Malolan. "Causality Representation and Time Warp Optimizations." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1171055502.
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Roy, Sukumar Chandra. "Knowledge and causality : a critical analysis." Thesis, University of North Bengal, 2007. http://hdl.handle.net/123456789/62.
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Pramanik, Ananda. "The Concept of causality some clarifications." Thesis, University of North Bengal, 2002. http://hdl.handle.net/123456789/46.
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Moussa, Kouamé Richard. "Causalité en sciences sociales : quelques applications en microéconométrie appliquées à l'économie de la santé et du travail." Thesis, Cergy-Pontoise, 2016. http://www.theses.fr/2016CERG0816/document.
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L'objectif général de cette thèse est d'une part de rendre compte du traitement économétrique des phénomènes de causalité en sciences sociales et d'autre part de proposer des applications à l'étude de la causalité entre la santé et l’emploi du travail, à l’étude des décisions de retraite anticipée en relation avec la santé, la richesse et les préférences pour le futur.Pour l’analyse de la causalité santé-emploi, deux approches sont utilisées dans le cadre d’une analyse ex-post. L’approche paramétrique qui consiste en l’estimation d’un probit bivarié en panel qui inclut les valeurs retardées des variables expliquées parmi les explicatives afin de mesurer la causalité au sens de Granger entre les deux phénomènes. Le problème d’endogénéité est ainsi résolu. Les conditions initiales sont prises en compte via des équations spécifiques. Les effets individuels rendent compte de l’hétorogénéité individuelle dans l’analyse de la causalité. La seconde approche est non paramétrique et basée sur la mesure de causalité de Kullback. Elle permet de mesurer la dynamique des relations de causalité ainsi que ses déterminants.Quant à l’analyse de la décision de retraite anticipée, elle est faite grâce à un modèle structurel dynamique. Ce modèle intègre des fonctions de production et de consommation de stock de santé ainsi qu’une fonction d’utilité inter temporelle. Les conditions d’équilibre de ce modèle permettent de prédire les probabilités de retraite anticipéeThe main objective of this thesis is to investigate on the econometric treatment of causality in social sciences and to provide some applications on the establishment of causality between health condition and job status, and on the early retirement decision based on health, estate and preferences for future.To analyze the causality between health and job statuses, two approaches are used in the ex-post framework. The parametric approach involves estimating a bivariate probit panel model that includes lagged values of the dependent variables as explanatory to measure Granger causality. Thus, the problem of endogeneity is accounted for. The initial conditions are accounted for by introducing specific equations. Individual effects allow dealing with individual heterogeneity. The second approach is a nonparametric one and is based on the Kullback causality measures. This approach allows measuring the dynamic of the causal links and its determinants.For analyzing the early retirement decision, we use a dynamic structural model. This model deals with health stock production and consumption functions, and with an inter temporal utility function. The first order conditions of the model allow predicting the probabilities of early retirement
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Minto, William Richmond. "Foundations for a realist theory of causality." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ28507.pdf.
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Meganck, Stijn. "Towards an Integral Approach for Modeling Causality." Phd thesis, INSA de Rouen, 2008. http://tel.archives-ouvertes.fr/tel-00915256.
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A partir de données d'observation classiques, il est rarement possible d'arriver à une structure de réseau bayésien qui soit complètement causale. Le point théorique auquel nous nous intéressons est l'apprentissage des réseaux bayésiens causaux, avec ou sans variables latentes. Nous nous sommes d'abord focalisés sur la découverte de relations causales lorsque toutes les variables sont connues (i.e. il n'y a pas de variables latentes) en proposant un algorithme d'apprentissage utilisant à la fois des données issues d'observations et d'expérimentations. Logiquement, nous nous sommes ensuite concentrés sur le même problème lorsque toutes les variables ne sont pas connues. Il faut donc découvrir à la fois des relations de causalité entre les variables et la présence éventuelle de variables latentes dans la structure du réseau bayésien. Pour cela, nous tentons d'unifier deux formalismes, les modèles causaux semi-markoviens (SMCM) et les graphes ancestraux maximaux (MAG), utilisés séparément auparavant, l'un pour l'inférence causale (SMCM), l'autre pour la découverte de causalité (MAG). Nous nous sommes aussi interessé à l'adaptation de réseaux bayésiens causaux pour des systèmes multi-agents, et sur l'apprentissage de ces modèles causaux multi-agents (MACM).
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Shanks, D. R. "Event contingencies and the judgement of causality." Thesis, University of Cambridge, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.355513.
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Zou, Cunlu. "Applications of Granger causality to biological data." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/35694/.
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In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data. In literature, there are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality. To apply the four different approaches to the same problem, a key issue is to choose which approach is used to tackle the data, in particular when they give rise to contradictory results. In this thesis, I provided an answer by focusing on a systematic and computationally intensive comparison between the two common approaches which are dynamic Bayesian network inference and Granger causality. The comparison was carried out on both synthesized and experimental data. It is concluded that the dynamic Bayesian network inference performs better than the Granger causality approach, when the data size is short; otherwise the Granger causality approach is better. Since the Granger causality approach is able to detect weak interactions when the time series are long enough, I then focused on applying Granger causality approach on real experimental data both in the time and frequency domain and in local and global networks. For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered. In addition to gene and protein data, Granger causality approach was also applied on Local Field Potential (LFP) data. Here we have combined multiarray electrophysiological recordings of local field potentials in both right inferior temporal (rIT) and left IT (lIT) and right anterior cingulate (rAC) cortices in sheep with Granger causality to investigate how anaesthesia alters processing during resting state and exposure to pictures of faces. Results from both the time and frequency domain analyses show that loss of consciousness during anaesthesia is associated with a reduction/disruption of feed forward open-loop cortico-cortical connections and a corresponding increase in shorter-distance closed loop ones.
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Fennell, Damien James. "A philosophical analysis of causality in econometrics." Thesis, London School of Economics and Political Science (University of London), 2005. http://etheses.lse.ac.uk/1855/.
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This thesis makes explicit, develops and critically discusses a concept of causality that is assumed in structural models in econometrics. The thesis begins with a development of Herbert Simon's (1953) treatment of causal order for linear deterministic, simultaneous systems of equations to provide a fully explicit mechanistic interpretation for these systems. Doing this allows important properties of the assumed causal reading to be discussed including: the invariance of mechanisms to intervention and the role of independence in interventions. This work is then extended to basic structural models actually used in econometrics, linear models with errors-in-the-equations. This part of the thesis provides a discussion of how error terms are to be interpreted and sets out a way to introduce probabilistic concepts into the mechanistic interpretation set out earlier. The resulting analysis is then critically compared with similar work by economists, Stephen LeRoy (1995) and Kevin Hoover (2001a) who both develop Simon's work on causal order in different ways. In the latter part of the thesis, the mechanistic interpretation set out at the beginning is used to interpret identification conditions. Typically, these are presented in econometrics as mathematical conditions for determining whether unknown parameters in equations can be measured from observation. In the thesis it is shown that the identification conditions imposed on sets of equations when interpreted mechanistically require a sparseness of causal structure that ensures that experiments are hypothetically possible of the causal structure. It also analyses the role of identifiability conditions in causal inference. The final part of the thesis shows that the mechanistic interpretation developed in the thesis succeeds, unlike Simon's own methods for analysing spurious correlation, in avoiding important criticisms by Nancy Cartwright (1989) whose own approach to inferring causal structure from observations is also critically analysed.
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Freedman, David Emmanuel. "The intentionality, causality and metaphysics of naming." Thesis, University of Oxford, 1988. http://ora.ox.ac.uk/objects/uuid:e45f5865-b9a9-46a1-b8b7-a26eb4ae58fa.
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This thesis delineates the boundaries of theories of naming, placing particular emphasis on the demarcation of the semantic function of referring expressions from the pragmatics of referential usage. The need for such emphasis derives from frequent confusions in work on singular reference where pragmatic phenomena are used to refute semantic theories and vice versa. Part One of the thesis examines various conceptions of the semantic function of names. Frege's notion of 'Sinn' is shown to be incoherent as are its reincarnations by modern, neo-Fregean commentators: a more modest role for sense is suggested. Kripke's intuitions about scope-insensitivity in modal contexts are amplified and criticised. An investigation of singular thought yields a discussion of Evans 1 views and the removal of the apparent impasse that substitutevity failure causes for Millian theories. Part Two of the thesis concentrates on the mechanism of namereference, which works by connecting a contemporary use of a name via a causal umbilicus to some (or no) supposed referent. A number of anomalies are examined, from which the notion of a name-using practice and an elucidation of the criteria in virtue of which we judge a term to be a name are developed. In Part Three of the thesis the artificial super imposition of logical structure onto natural language is explored. The argument hinges on a plausible generalization of Donnellan's referentialattributive distinction. From this there emerges a picture of the complex network of intentions that surround the referring process. The role of definite descriptions in grounding the use of names is explained. Both the semantic and pragmatic pictures of name-reference are applied to the perennial puzzles of substitutivity failure and existential statements. The results underline the fact that a great deal of work needs to be done by those who subscribe to the growing orthodoxy of Millian theories, but that this is work in pragmatic areas that are too often ignored by those studying 'pure' semantics.
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Constantinou, Panayiota. "Conditional independence and applications in statistical causality." Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708164.
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Hayden, Douglas. "Causality, uncertainty and falsification in clinical research." Thesis, Boston University, 2012. https://hdl.handle.net/2144/31566.
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Thesis (Ph.D.)--Boston UniversityPLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.
We address two foundational issues of inductive reasoning and related applications. We first consider the problem of inferring the causal effect of active versus control treatment in randomized clinical trials. We regard the pairwise differences in outcome between active and control subjects as fixed quantities which may or may not be observed depending on treatment allocation. Causal inference consists of observing a sample of pairwise differences in order to estimate the mean of all possible pairwise differences, which constitutes the complete causal effect. However, because this complete effect is unobservable, there is an unavoidable observational uncertainty, which is a fundamental feature of the physical world. We follow with a focus on the problem of falsification of scientific claims based on experimental data. A scientific claim resulting from an experiment is a function of the observed data, which induces a stochastic model on the space of possible claims, and a probability of falsification by a follow-up experiment. The map from the data into the claim space, the structure of the claim space, and the claim falsification probability, provide a mathematical structure for exploring the statistical approach to inductive reasoning. We then turn from foundational issues and causal inference to the weaker question of association in the high-dimensional setting of microarray studies. We propose a permutation test based on the matrix of pairwise distances between individual genomic profiles to test for an association between a binary classifier and gene expression. If the two groups defined by the binary classifier differ in gene expression then the mean between group distance between profiles should exceed the mean within group distance. Finally, we extend the study of association in genomic studies to consider the accuracy of genomic-based prediction of uncomplicated recovery from severe trauma, for which we seek upper bounds. In this case the dimension of the model space precludes an exhaustive model search, so that we bracket this space in the two dimensions of smoothness and complexity. A sequence of models can be thought of as a visualization of the structure of a cross-section of the gene-outcome space.
2031-01-01
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Gerstenberg, T. "Making a difference : responsibility, causality and counterfactuals." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1383490/.
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In this thesis, I develop a general framework of how people attribute responsibility. In this framework, people's responsibility attributions are modelled in terms of counterfactuals defined over a causal representation of the situation. A person is predicted to be held responsible to the extent that their action made a difference to the outcome. Accordingly, when attributing responsibility we compare what actually happened with the outcome in a simulated counterfactual world in which the person's action had been different. However, a person can still be held responsible for an outcome even if their action made no difference in the actual situation. Responsibility attributions are sensitive to whether a person's action would have made a difference in similar counterfactual situations. Generally, responsibility decreases with the number of events that would have needed to change from the actual situation in order to generate a counterfactual situation in which the person's action would have been pivotal. In addition to how close a person was to being pivotal, responsibility attributions are influenced by how critical a person's action was perceived prior to the outcome. The predictions derived from this general framework are tested in a series of experiments that manipulate a person's criticality and pivotality by varying the causal structure of the situation and the person's mental states. The results show that responsibility between the members of a group diffuses according to the causal structure which determines how individual contributions combine to yield a joint outcome. Differences in the group members' mental states, such as their knowledge about the situation, their expectations about each other's performance as well as their intentions, also affect attributions. Finally, I demonstrate how this general framework can be extended to model attributions for domains in which people have rich, intuitive theories that go beyond what can be expressed with simple causal models.
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Mihaila, Claudiu. "Discourse causality recognition in the biomedical domain." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/discourse-causality-recognition-in-the-biomedical-domain(c3a4290c-276e-414f-b843-096564a892d3).html.
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With the advent of online publishing of scientific research came an avalanche of electronic resources and repositories containing knowledge encoded in some form or another. In the domain of biomedical sciences, research is now being published at a faster-than-ever pace, with several thousand articles per day. It is impossible for any human being to process that amount of information in due time, let alone apply it to their own needs. Thus appeared the necessity of being able to automatically retrieve relevant documents and extract useful information from text. Although it is now possible to distil essential factual knowledge from text, it is difficult to interpret the connections between the extracted facts. These connections, also known as discourse relations, make the text coherent and cohesive, and their automatic discovery can lead to a better understanding of the conveyed knowledge. One fundamental discourse relation is causality, as it is the one which explains reasons and allows for inferences to be made. This thesis is the first comprehensive study which focusses on recognising discourse causality in biomedical scientific literature. We first construct a manually annotated corpus of discourse causality and analyse its characteristics. Then, a methodology for automatically recognising causal relations using text mining and natural language processing techniques is presented. Furthermore, we investigate the automatic identification of additional information about the polarity, certainty, knowledge type and source of causal relations. The entire methodology is evaluated by empirical experiments, whose results show that it is possible to successfully extract causal relations from biomedical literature. Finally, we provide an example of a direct application of our research and offer ideas for further research directions and possible improvements to our methodology.
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Sen, Maya. "Essays on Causality, Race, and the Law." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10333.
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Making causal inferences about race is difficult because no means exist to manipulate units into treatment and control groups. Chapter 1 addresses this predicament. First, I argue that race should be defined as a composite measure in which some elements are mutable. Second, I note that identifying the units of analysis is particularly important when thinking about race. These extensions allow us to synthesize two instances in which causal inferences regarding race may be permissible: (1) studies that measure the effect of exposing an entity to a racial cue and (2) studies that disaggregate race into constituent pieces and measure the effect of a mutable element. Chapters 2 and 3 provide examples of the first “exposure” approach in the context of judicial politics. Chapter 2 analyzes the role of race and gender in judicial confirmations and demonstrates that minority and female nominees to federal courts are awarded lower qualification ratings by the American Bar Association (ABA) than are white and male nominees. This is the case even when comparing only judges with similar education, ideologies, and experiences. Furthermore, I present results showing that ABA qualification scores are not predictive of judges’ reversal rates. Chapter 3 explores what happens once minority judges are confirmed. Focusing specifically on African Americans, I show that opinions authored by black judges are overturned more than cases authored by whites. The effect is robust and persists after taking into account measures of judicial qualifications, previous professional and judicial experience, and partisanship. Taken together, Chapters 2 and 3 have clear implications: despite attempts to make judiciary more reflective of the U.S. population, racial disparities continue to persist.Government
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Binkyte-Sadauskiene, Ruta. "Advancing Ethical AI : Methods for fairness enhancement leveraging on causality and under privacy constraints." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAX145.
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L'intelligence artificielle éthique et la causalité sont des domaines interconnectés qui revêtent une importance croissante dans notre société numérique. L'éthique en intelligence artificielle (IA) implique la recherche de compromis complexes, que ce soit dans la structure globale de l'IA ou dans des domaines spécifiques comme l'équité et la protection de la vie privée. Ces compromis nécessitent un équilibre délicat entre les principes éthiques fondamentaux et les contraintes pratiques des systèmes d'IA.Un défi majeur dans la recherche de l'équité en IA réside dans la tension entre la précision et l'équité. Les méthodes traditionnelles, telles que la parité statistique, ont tendance à compromettre la précision lors de la correction des biais dans les données, en particulier lorsque des corrélations existent entre les attributs sensibles et les facteurs légitimes de prise de décision. Pour atténuer ce problème, nous introduisons "BaBE" (Élimination des Biais Bayésiens), une approche novatrice combinant l'inférence bayésienne et la méthode de maximisation de l'espérance. BaBE est capable d'estimer les variables explicatives latentes, permettant ainsi des prises de décision équitables sans sacrifier la précision.De plus, nous abordons les défis posés par la sous-représentation et le biais d'échantillonnage en apprentissage automatique. La mesure précise de la discrimination est essentielle pour évaluer l'équité. Pour éliminer le biais d'échantillonnage, nous introduisons des variantes telles que le biais de taille d'échantillon et le biais de sous-représentation. Nous décomposons également la discrimination en variance, biais et bruit, offrant ainsi une compréhension complète des sources de biais dans les systèmes d'IA.La protection de la vie privée est une dimension cruciale de l'IA éthique, impliquant souvent un compromis entre la protection des données et l'utilité. La confidentialité différentielle locale offre aux fournisseurs de données la possibilité d'appliquer des mécanismes d'obscurcissement individuellement, garantissant ainsi la protection de la vie privée, même dans des environnements non fiables. Cependant, l'introduction de bruit dans ce contexte peut perturber les corrélations des données, notamment celles nécessaires à l'apprentissage des structures de causalité.La causalité, avec son regain d'intérêt au XXIe siècle, joue un rôle essentiel dans la compréhension du monde et dans la prise de décisions équitables et automatisées. Nous soulignons l'importance de la causalité dans l'évaluation de l'équité, tant du point de vue légal que quotidien. La pensée causale offre un cadre solide pour distinguer entre prédictions causales et non causales, mettant en lumière l'impact social de ces dernières.L'intersection entre l'intelligence artificielle éthique et la causalité offre une voie prometteuse pour aborder les compromis et renforcer les fondements éthiques des systèmes d'intelligence artificielle. En tirant parti de la causalité dans les domaines de l'équité, de la vie privée et de la prise de décision, nous pouvons développer des systèmes d'IA qui respectent non seulement les principes éthiques, mais qui excellent également en termes de précision, d'équité et de protection de la vie privéeThe field of Ethical AI is characterized by complex trade-offs, both within its overarching framework and across specialized domains like fairness and privacy. These trade-offs often involve navigating the delicate balance between fundamental ethical principles and the practical requirements of artificial intelligence systems. This extended abstract explores how causality can serve as a potent tool to address these trade-offs and foster synergies among various aspects of Ethical AI. We delve into three key areas: Fairness, Privacy, and Causality.In the pursuit of fairness in AI, a common trade-off is the tension between accuracy and fairness. Traditional methods like statistical parity often compromise accuracy when addressing bias in data, especially when a correlation exists between sensitive attributes and legitimate decision-making factors. To mitigate this issue, we introduce "BaBE" (Bayesian Bias Elimination), an innovative approach that combines Bayesian inference and the Expectation-Maximization method. BaBE estimates the latent explaining variables, enabling fair decision-making without sacrificing accuracy. Our experiments on synthetic and real datasets demonstrate the effectiveness of BaBE in achieving a high level of fairness and accuracy.Additionally, we shed light on the challenges posed by underrepresentation and sampling bias in machine learning. Accurate measurement of discrimination is pivotal in assessing fairness. To disambiguate the concept of sampling bias, we introduce clear variants, such as sample size bias (SSB) and underrepresentation bias (URB). We also decompose discrimination into variance, bias, and noise, providing a comprehensive understanding of the sources of bias in AI systems. Our approach contributes to a nuanced evaluation of fairness and informs strategies for addressing bias in machine learning.Privacy is another critical dimension in Ethical AI, and here, the trade-off often involves balancing data protection with utility. Local differential privacy offers data providers the ability to apply obfuscation mechanisms individually, ensuring privacy even in untrusted environments. However, the addition of noise in this context can distort data correlations, particularly affecting causal-structure learning. We analyze various locally differentially private mechanisms to understand the trade-off between privacy and the accuracy of causal structure learning algorithms when applied to obfuscated data. Our findings provide valuable insights for selecting appropriate local differential privacy protocols for causal discovery tasks, preserving user data privacy while conducting meaningful analyses.Causality, with its resurgence in the 21st century, plays a pivotal role in understanding the world and making fair, automated decisions. We emphasize the importance of causality in evaluating fairness, both from a legal and everyday perspective. Causal reasoning offers a robust framework for distinguishing between causal and non-causal predictions, highlighting the social impact of the latter. We provide arguments and examples showcasing the significance of causality in fairness evaluation and anti-discrimination processes. However, we acknowledge the challenges and limitations of applying causality in practical scenarios, and we discuss potential solutions to overcome these obstacles.The intersection of Ethical AI and causality presents a promising avenue for addressing trade-offs and enhancing the ethical foundations of artificial intelligence systems. By leveraging causality in fairness, privacy, and decision-making, we can develop AI systems that not only adhere to ethical principles but also excel in terms of accuracy, fairness, and privacy protection. This extended abstract offers a glimpse into the intricate landscape of Ethical AI and its potential to create a more ethical and equitable AI-driven future
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Espinosa, Javier. "The causality and characterization of the widowhood effect." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3745.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2006.Thesis research directed by: Economics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Stiassny, Alfred, and Stephan Koren. "The Temporal Causality between Government Taxes and Spending." Inst. für Volkswirtschaftstheorie und -politik, WU Vienna University of Economics and Business, 1992. http://epub.wu.ac.at/6288/1/WP_14.pdf.
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Shenvi, Goutami. "Spatio-temporal effects on the perception of causality." Berlin Logos-Verl, 2005. http://deposit.ddb.de/cgi-bin/dokserv?id=2686931&prov=M&dok_var=1&dok_ext=htm.
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Kupriyanov, Andrey [Verfasser], and Bernd [Akademischer Betreuer] Finkbeiner. "Causality-based verification / Andrey Kupriyanov ; Betreuer: Bernd Finkbeiner." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2016. http://d-nb.info/1120985013/34.
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Syer, David. "Dynamics in accretion theory : variability, eccentricity and causality." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259654.
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Hoover, K. D. "Causality and invariance in the money supply process." Thesis, University of Oxford, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.371665.
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Thorniley, James. "Information transfer and causality in the sensorimotor loop." Thesis, University of Sussex, 2015. http://sro.sussex.ac.uk/id/eprint/57186/.
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This thesis investigates information-theoretic tools for detecting and describing causal influences in embodied agents. It presents an analysis of philosophical and statistical approaches to causation, and in particular focuses on causal Bayes nets and transfer entropy. It argues for a novel perspective that explicitly incorporates the epistemological role of information as a tool for inference. This approach clarifies and resolves some of the known problems associated with such methods. Here it is argued, through a series of experiments, mathematical results and some philosophical accounts, that universally applicable measures of causal influence strength are unlikely to exist. Instead, the focus should be on the role that information-theoretic tools can play in inferential tests for causal relationships in embodied agents particularly, and dynamical systems in general. This thesis details how these two approaches differ. Following directly from these arguments, the thesis proposes a concept of “hidden” information transfer to describe situations where causal influences passing through a chain of variables may be more easily detected at the end-points than at intermediate nodes. This is described using theoretical examples, and also appears in the information dynamics of computer-simulated and real robots developed herein. Practical examples include some minimal models of agent-environment systems, but also a novel complete system for generating locomotion gait patterns using a biologically-inspired decentralized architecture on a walking robotic hexapod.
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Fu, Carolyn J. "Collective causality : building solution architectures with a crowd." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112063.
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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, System Design and Management Program, 2017.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 54-55).
Traditional open innovation has operated on the assumption that by casting a wide net into the crowd, the likelihood of obtaining a desirable solution to a problem increases, due to the greater range of potential solutions that is obtained. This is typically implemented using a competitive format, where the best ideas are selected from a crowd, and the rest are discarded. Unfortunately, the drawback of such a format is that it fails to make use of the efforts behind discarded ideas. Each of these ideas represents a great deal of cognitive effort that has gone towards understanding and solving a problem, and discarding them sacrifices potentially useful insights that might be derived from ultimately unworkable solutions. This thesis explores how a more effective form of collective intelligence might be obtained - one where the half-baked solutions of many participants might be combined to produce something more effective than one participant's fully baked solution that is selected through competition. The specific format of a collaborative causal map is explored, where individuals can each contribute causes and causal links to an overall causal web, building an ever richer architecture of potential solutions (and their sub-solutions) to an overall problem. The goal is to integrate individuals' contributions such that they accumulate to an overall cohesive solution that is better than what any individual could have developed. A series of pilots are conducted to understand the group dynamics in both offline and online collaboration, and determine those factors that are material to the success of an online collaborative causal map. Such factors include how the question is framed, how users attend to others' contributions, or how users' contributions can be curated. These factors are ultimately incorporated into a prototype collaborative causal mapping website, which is developed for public use.
by Carolyn J. Fu.
S.M. in Engineering and Management
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Kim, Jaehyun 1970. "Causality and sensitivity analysis in distributed design simulation." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8329.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, February 2002.Includes bibliographical references (leaves 109-111).
Numerous collaborative design frameworks have been developed to accelerate the product development, and recently environments for building distributed simulations have been proposed. For example, a simulation framework called DOME (Distributed Object-oriented Modeling and Evaluation) has been developed in MIT CADlab. DOME is unique in its decentralized structure that allows heterogeneous simulations to be stitched together while allowing proprietary information an simulation models to remain secure with each participant. While such an approach offers many advantages, it also hides causality and sensitivity information, making it difficult for designers to understand problem structure and verify solutions. The purpose of this research is to analyze the relationships between design parameters (causality) and the strength of the relationships (sensitivity) in decentralized web-based design simulation. Algorithms and implementations for the causality and sensitivity analysis are introduced. Causality is determined using Granger's definition of causality, which is to distinguish causation from association using conditional variance of the suspected output variable. Sensitivity is estimated by linear regression analysis and a perturbation method, which transfers the problem into a frequency domain by generating periodic perturbations. Varying Internet latency and disturbances are issues with these methods. Thus, algorithms are developed and tested to overcome these problems.
by Jaehyun Kim.
Ph.D.
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Wilson, Joanna Katherine. "Violent-eye literature : contemporary American narratives of causality." Thesis, University of Leicester, 2017. http://hdl.handle.net/2381/40766.
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During the 1990s, a number of violent homodiegetic narrators appeared in what I call “violent-eye fiction”, that which is written from the continually immersive first-person perspective of a violent protagonist. This sub-genre of transgressive fiction is used in this thesis to question whether a first-person protagonist can ever be a completely unsympathetic character, or whether narratives of causality reconfigure violent narrators into multifaceted, complex, and ultimately familiar individuals. Structured around four chapters, the thesis takes a comparative and thematic approach that enables me to argue that violent-eye texts are ultimately narratives of causality by charting the progression of the violent-eye protagonist out of childhood, into adolescence, and ultimately into adulthood, with the first three chapters reflecting this movement. Aetiological violence is the subject of the first two chapters, with childhood trauma and mother blame explored in Chapter One through textual analysis of A. M. Homes’ The End of Alice (1996a) and Jeff Lindsay’s Dark Dreaming Dexter (2004), and adolescent trauma and absent fathers analysed in Chapter Two in relation to Chuck Palahniuk’s Fight Club (1996) and Don de Grazia’s American Skin (1998). Ontological violence is the subject of Chapter Three, in which adult violence and sexual desire are explored, particularly in relation to the problematic conflation in some novels of violence and homosexuality, including Joyce Carol Oates’ Zombie (1995) and Poppy Z. Brite’s Exquisite Corpse (1996). Finally, Chapter Four looks at Stephen King’s Rage (1977a) and Lionel Shriver’s We Need to Talk about Kevin (2003a) in order to explore the absence of school shooters in 1990s fiction. This discussion brings the thesis full circle by returning to reassess the concept of the unsympathetic character and showing how the absence of school shooters from violent-eye fiction of the 1990s onwards arguably indicates that they fall into this category.
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Reimann, Sebastian Michael. "Multilingual Zero-Shot and Few-Shot Causality Detection." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446516.
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Relations that hold between causes and their effects are fundamental for a wide range of different sectors. Automatically finding sentences that express such relations may for example be of great interest for the economy or political institutions. However, for many languages other than English, a lack of training resources for this task needs to be dealt with. In recent years, large, pretrained transformer-based model architectures have proven to be very effective for tasks involving cross-lingual transfer such as cross-lingual language inference, as well as multilingual named entity recognition, POS-tagging and dependency parsing, which may hint at similar potentials for causality detection. In this thesis, we define causality detection as a binary labelling problem and use cross-lingual transfer to alleviate data scarcity for German and Swedish by using three different classifiers that make either use of multilingual sentence embeddings obtained from a pretrained encoder or pretrained multilingual language models. The source languages in most of our experiments will be English, for Swedish we however also use a small German training set and a combination of English and German training data. We try out zero-shot transfer as well as making use of limited amounts of target language data either as a development set or as additional training data in a few-shot setting. In the latter scenario, we explore the impact of varying sizes of training data. Moreover, the problem of data scarcity in our situation also makes it necessary to work with data from different annotation projects. We also explore how much this would impact our result. For German as a target language, our results in a zero-shot scenario expectedly fall short in comparison with monolingual experiments, but F1-macro scores between 60 and 65 in cases where annotation did not differ drastically still signal that it was possible to transfer at least some knowledge. When introducing only small amounts of target language data, already notable improvements were observed and with the full German training data of about 3,000 sentences combined with the most suitable English data set, the performance for German in some scenarios even almost matches the state of the art for monolingual experiments on English. The best zero-shot performance on the Swedish data was even outperforming the scores achieved for German. However, due to problems with the additional Swedish training data, we were not able to improve upon the zero-shot performance in a few-shot setting in a similar manner as it was the case for German.
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Beckman, David Eugene Preskill John P. "Investigations in quantum computing: causality and graph isomorphism /." Diss., Pasadena, Calif. : California Institute of Technology, 2004. http://resolver.caltech.edu/CaltechETD:etd-05272004-174253.
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Chambers, Natalie Rae. "Turkey: The Causality Dilemma of Economics and Politics." Thesis, The University of Arizona, 2013. http://hdl.handle.net/10150/297541.
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This thesis examines if Turkey’s economic relations with the European Union have impacted its political orientation between the European Union and the Middle East. The research analyzes trade flows between Turkey and these two regions while utilizing statistics on embassy establishments, high level representative visits, and events both conflict and cooperative, sent from one country's policy makers to another to understand Turkey’s recent political and economic trends. Determining each region’s actual involvement compared to their available involvement in trade and political relations, this thesis examines potential shifts over the past 12 years while looking at data that extends back 60 years. The results indicate found that although there are shifts in Turkey’s economic relations with the Middle East and the European Union, they were preceded by political shifts since the 1980’s. This was determined through significant increases in diplomatic ties with the Middle East primarily in the 20 years before the increase in trade with the Middle East developed. Data analysis showed there was no apparent correlation between number of events sent or received and the percent of trade that was shared with each region.
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Nguyen, Van Hai. "Formalizing Time and Causality in Polychronous Polytimed Models." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS282/document.
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L'intégration de composants dans un système peut s'avérer difficile lorsque ces composants ont été conçus selon différents paradigmes ou s'ils se basent sur différents cadres de temps devant être synchronisés. Cette synchronisation peut être dirigée par les évènements (un évènement est provoqué par un autre), ou dirigée par le temps (un évènement se produit parce qu'il en est l'heure). En considérant que chaque composant admet son propre cadre de temps et qu'ils peuvent ne pas être reliés, il est possible qu'une unique ligne de temps globale n'existe pas.Nous nous intéressons à la spécification de schémas de synchronisation pour de tels systèmes polychrones et polytemporisés. Notre étude nous a mené à la conception de modèles sémantiques pour un langage temporisé à évènements discrets, appelé TESL et développé par Boulanger et al. Ce langage a été utilisé pour coordonner la simulation de modèles composites et pour tester l'intégration de systèmes.Dans cette thèse, nous présentons une sémantique dénotationnelle fournissant une compréhension précise et logiquement cohérente du langage. Puis nous proposons une sémantique opérationnelle afin de dériver des traces d'exé-cutions satisfaisant une spécification TESL. Celui-ci a été utilisé pour les problématiques de test des systèmes, à travers l'implantation d'un solveur nommé Heron. Pour résoudre la question de cohérence et de correction de ces règles sémantiques, nous avons également développé une sémantique intermédiaire coinductive reliant les deux sémantiques dénotationnelles et opérationnelles. Nous établissons des propriétés sur la relation entre les deux sémantiques: correction, complétude, progrès ainsi que terminaison locale. Enfin, notre formalisation ainsi que les preuves associées ont été entièrement mécanisées dans l'assistant de preuve Isabelle/HOLIntegrating components into systems turns out to be difficult when these components were designed according to different paradigms or when they rely on different time frames which must be synchronized. This synchronization may be event-driven (an event occurs because another event occurs) or time-driven (an event occurs because it is time for it to occur). Considering that each component admits its own time frame, and that they may not be related, a unique global time line may not exist.We are interested in specifying synchronization patterns for such polychronous and polytimed systems. Our study had led us to design semantic models for a timed discrete-event language, called the TESL language developed by Boulanger et al. This language has been used for coordinating the simulation of composite models and testing system integration.In this thesis, we present a denotational semantics providing an accurate and logic-consistent understanding of the language. Then we propose an operational semantics to derive satisfying runs from TESL specifications. It has been used for testing purposes, through the implementation of a solver, named Heron. To tackle the issue of the consistency and correctness of these semantic rules, we developed a co-inductive intermediate semantics that relates both the denotational and the operational semantics. Then we establish properties over the relation of our semantic models: soundness, completeness and progress, as well as local termination. Finally, our formalization and these proofs have been fully mechanized in the Isabelle/HOL proof assistant
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Caporin, Massimiliano. "Long memory conditional heteroskedasticity and second order causality." Doctoral thesis, Università Ca' Foscari Venezia, 2003. http://hdl.handle.net/10579/785.
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In questa tesi estendo la letteratura corrente riguardo ai modelli GARCH a memoria lunga.
Dapprima considero il problema della stazionarietà, evidenziando il motivo per il quale le
definizioni correnti sono inadeguate, ricavando inoltre le condizioni di stazionarietà utilizzando un recente teorema di Zaffaroni (2000). Mi sono poi concentrato sul problema della stima rilevando
l'inapplicabilità della dimostrazione di Lee ed Hansen (1991) sulla consistenza dello stimatore di
quasi massima verosimiglianza, risultato contrario a quanto sostenuto da Baillie, Bollerslev e
Mikkelsen (1996). Suggerisco quindi una strada alternativa, utilizzando un recente lavoro di
Jeantheau (1998), evidenziandone tuttavia i limiti. Di conseguenza ho verificato la consistenza e la
correttezza delle stime di quasi massima verosimiglianza all'interno di un esperimento Montecarlo,
il cui scopo principale era invece quello dello studio dell'identificazione della memoria lunga nelle varianze condizionali. Questo studio Montecarlo dimostra che i criteri di informazione individuano correttamente la memoria lunga, mentre i test di correlazione ed effetti ARCH sui residui sono in sostanza inutili.
Ho poi considerato l'analisi della previsione delle varianze, assumendo un generatore a memoria
lunga per le varianze condizionali. Ho derivato l'equazione del previsore della varianza, estendendo
in questo senso un lavoro di Baillie e Bollerslev (1991). In seguito ho affrontato il problema del
calcolo del Value-at-Risk quando utilizziamo un modello mal specificato per la varianza. L'analisi è
stata condotta all'interno della classe dei modelli GARCH utilizzando un approccio Montecarlo. Ho
studiato quindi gli effetti di un'errata specificazione quando i dati sono generati da un modello
FIGARCH e per il VaR si utilizzano modelli a memoria breve. Ho confrontato i diversi modelli per
il VaR tramite un gruppo di test e con un approccio basato su funzioni di perdita. Posso così
dimostrare che il modello correttamente specificato permette una migliore stima del VaR, un
risultato atteso a priori. Ho esteso quindi l'analisi considerando anche gli effetti dell'aggregazione sul comportamento a memoria lunga delle varianze e sul calcolo del VaR. Tramite un esperimento Montecarlo verifico che la memoria lunga è robusta al processo di aggregazione, tuttavia tale comportamento è influenzato anche dall'ampiezza della memoria stessa. Per quanto riguarda il
confronto delle misure VaR ottenute da dati aggregati e non aggregati, verifico che o dati aggregati
permettono si ottenere stime migliori.
Ho infine considerato un problema diverso, l'individuazione della causalità del secondo ordine (tra
varianze). Dopo il riesame della letteratura sul tema estendo dapprima le definizioni ed i requisiti
teorici per ottenere la non causalità in un modello generale del tipo VARMA-GARCH-M.
Considero poi le diverse specificazioni GARCH multivariate utilizzate in letteratura per la stima
della causalità del secondo ordine, ne evidenzio i punti deboli e suggerisco quindi un nuovo
modello GARCH bivariato il cui scopo è quello di individuare non solo la presenza di causalità del
secondo ordine ma anche la sua direzione. Il modello viene poi testa in un'applicazione pratica.
Presento dapprima tutte le analisi preliminari alla costruzione delle serie dei volumi e dei
rendimenti del FIB30, utilizzando un database fornito dalla Borsa Italiana. Sulle serie filtrate ho
applicato diversi modelli per la causalità tra varianze, incluso quello suggerito nel presente lavoro.
Ho ottenuto il risultato atteso, vale a dire l'importanza della direzione di causalità tra varianze.
In this dissertation I extend the current literature on long memory GARCH processes. I considered
at first the stationarity problem, showing why current definitions are inadequate and deriving
stationarity conditions using a recent theorem of Zaffaroni (2000). I focused then on the estimation
problem, pointing out the inapplicability of the well known Lee and Hansen (1991) proof of consistency of quasi maximum likelihood estimators, the opposite of the claims of Baillie,
Bollerslev and Mikkelsen (1996). Therefore, I suggested the application of a recent study of
Jeantheau (1998), showing the limits of this approach. I showed then consistency and umbiasedness
of the Quasi Maximum Likelihood estimators within a Montecarlo experiment whose primary purpose was the study of the identification of long memory in conditional variances. This Montecarlo study shows that information criteria correctly detect long memory, while tests on residual correlations and residual ARCH effects are useless.
I turn then to the analysis of variance forecasts when we assume a long memory DGP for the
conditional variances. I derive the equation of the variance forecaster extending a previous work of
Baillie and Bollerslev (1991). I consider then the problem of Value-at-Risk computation when we
misspecify the model for the variance. This analysis was done within the GARCH class of models
and in a Montecarlo framework. I studied the effects of misspecifications when the data are
generated by a FIGARCH process and we compute VaR with short memory specifications. I compared the different VaR models with a group of tests and with a loss function approach. I show that the correctly specified model allow for a finer VaR computation, as expected. I extended then the previous result considering also the effects of aggregation on the long memory behaviour of the
variances and on the VaR computation. I show with a Montecarlo experiment that long memory in
variance is robust to the aggregation process; however this behaviour is influenced by the memory
strength. Considering the comparison of VaR measure computed with aggregated and nonaggregated
data, I show that aggregated data are in general preferred.
Finally I considered a different problem, the detection of second order causality, which is among
variances. After a review of the current literature on this topic I extend the definitions and
theoretical requirements for non-causality in a general VARMA-GARCH-M model. I considered
then the different multivariate GARCH specifications used in the literature to detect second order
causality, showing their drawbacks and suggesting a new bivariate GARCH model whose purpose
is the detection of the causality existence and direction among variances. The suggested model is
then tested within an applied framework. I present all the analysis referred to the construction of the returns and volume series of the FIB30 market, using a transaction database supplied by the Borsa Italiana. On the filtered 5-minute series I applied then different models for causality among
variances, included the one I suggested, obtaining an expected results, the sign of causality matter.
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Arnoldi, Jakob. "Uncertain knowledge." Thesis, Goldsmiths College (University of London), 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270396.
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Fedderke, J. W. "The use of reason : an investigation into the source of the explanatory power of the concept of the optimising agent." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283953.
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Kim, Jinki. "Applications of non-linear time series models on finance and macroeconomics." Thesis, University of York, 2003. http://etheses.whiterose.ac.uk/10824/.
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Burda, Maike M. "Testing for causality with Wald tests under nonregular conditions." Doctoral thesis, [S.l.] : [s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=968852432.
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