Causal inference may help us to understand the underlying mechanisms and the risk factors of diseases. In Genetics, it is crucial to understand how the connectivity among variables is influenced by genetic and environmental factors. Family data have proven to be useful in elucidating genetic and environmental influences, however, few existing approaches are able of addressing structure learning of probabilistic graphical models (PGMs) and family data analysis jointly. We propose methodologies for learning, from observational Gaussian family data, the most likely PGM and its decomposition into genetic and environmental components. They were evaluated by a simulation study and applied to the Genetic Analysis Workshop 13 simulated data, which mimic the real Framingham Heart Study data, and to the metabolic syndrome phenotypes from the Baependi Heart Study. In neuroscience, one challenge consists in identifying interactions between functional brain networks (FBNs) - graphs. We propose a method to identify Granger causality among FBNs. We show the statistical power of the proposed method by simulations and its usefulness by two applications: the identification of Granger causality between the FBNs of two musicians playing a violin duo, and the identification of a differential connectivity from the right to the left brain hemispheres of autistic subjects. / Inferência causal pode nos ajudar a compreender melhor as relações de dependência direta entre variáveis e, assim, a identificar fatores de riscos de doenças. Em Genética, a análise de dados agrupados em famílias permite investigar influências genéticas e ambientais nas relações entre as variáveis. Neste trabalho, nós propomos métodos para aprender, a partir de dados Gaussianos agrupados em famílias, o mais provável modelo gráfico probabilístico (dirigido ou não dirigido) e também sua decomposição em dois componentes: genético e ambiental. Os métodos foram avaliados por simulações e aplicados tanto aos dados simulados do Genetic Analysis Workshop 13, que imitam características dos dados do Framingham Heart Study, como aos dados da síndrome metabólica do estudo Corações de Baependi. Em Neurociência, um desafio consiste em identificar interações entre redes funcionais cerebrais - grafos. Nós propomos um método que identifica causalidade de Granger entre grafos e, por meio de simulações, mostramos que o método tem alto poder estatístico. Além disso, mostramos sua utilidade por meio de duas aplicações: 1) identificação de causalidade de Granger entre as redes cerebrais de dois músicos enquanto tocam um dueto de violino e 2) identificação de conectividade diferencial do hemisfério cerebral direito para o esquerdo em indivíduos autistas.

La concession est une relation logique « secondaire » complexe qui se nourrit de plusieurs notions de base : l’hypothèse, la temporalité, la causalité, la négation, l’intensité, la subjectivité, etc. En reliant toutes ces notions, nous traitons les quatre locutions concessives les plus utilisées ainsi que leurs homologues dérivés de la même série en chinois : ji shi « bien que » « même si », sui ran « bien que » « malgré » (et zong ran « bien que » « même si »), na pa « bien que » « même si », jiu shi « même si » (et jiu suan « bien que » « même si »). L’objectif de notre travail est d’explorer le mécanisme concessif dans ces deux langues typologiquement distantes et différentes. En nous inscrivant en principe dans la même ligne que A. Meillet (1912), R. Martin (1982 et 1987) et O. Soutet (1987, 1990 et 1992), etc., nos recherches démontrent non seulement l’évolution diachronique de ce marqueur concessif étudié, mais mettent également en évidence ses rapprochements avec certains connecteurs analogues français (tels que cependant, alors, si, ja, nonobstant, etc.) pendant leur processus de grammaticalisation d’une manière à la fois sémantico-logique et syntaxico-discursive. Notre étude comprend divers corpus allant du chinois archaïque au chinois moderne, ainsi que des méthodologies linguistiques pour classer et traduire les exemples choisis. Nous constatons une ressemblance frappante entre la combinaison des notions de base au cours de la formation du mécanisme concessif dans les deux langues étudiées. / The concession is a “secondary” or “composed” linguistic concept since it combines many fundamental linguistic notions: hypothesis, temporality, causality, negation, intensity, subjectivity, etc. By relating all these crucial notions, our research examines the four most used concessive markers as well as their homologues derived from the same series in Chinese: ji shi "although" "even if", sui ran "although" "despite" (and zong ran "although" "even if"), na pa "although" "even if", jiu shi "even if" (and jiu suan "although" "even if"), as well as their equivalents in French based on previous theories and analyses in the two languages. The aim of the work is to explore the concessive mechanism in these two languages that are typologically distant and different. In the same line as A. Meillet (1912), R. Martin (1982 and 1987) and O. Soutet (1987, 1990 and 1992), etc., our research not only demonstrates the historical evolution of the studied concessive markers, but also highlights its rapprochements with certain French concessive connectors (such as cependant, alors, bien, si, ja, nonobstant, etc.) during their grammaticalization processes from semantic-logical and syntactic-discursive points of view. Our study involves various corpuses from archaic to contemporary Chinese and French, as well as technical linguistic methodologies to categorize and translate the extracted examples. The results of our present research show surprising similarities in terms of the concessive evolution despite the enormous etymological and cultural divergences of the two studied languages.

This thesis investigates if Environmental, Social and Governance (ESG) investments can be considered as an independent asset class. As ESG and responsible investing has increased substantially in recent years, responsible investments have entered the portfolios with other asset classes too. Therefore, there is a need in studying ESG investment properties with other financial asset classes. By collecting daily price data from October 2007 to December 2018, we research the directionalities between ESG, ethical, conventional, commodities and currency. Initially, we employed a MODWT, multiscale investment horizon wavelet analysis transformation of the data. The decomposed wavelet data is then applied in pairwise linear and non-linear Granger causality estimations to study the directionality relationships dependent on investment horizon. Additionally, econometric filtering processes have been employed to study the effects of volatility on directionality relationships. The results mainly suggest significant directionality relationships between ESG and the other asset classes. On the medium-term investment horizon, almost all estimations indicate strict bidirectionality. Thus, on the medium-term, ESG can be said to be integrated with the other asset classes. For the long-term horizon, most relationships are still predominantly bidirectional between ESG and all other asset classes. The biggest differences are found on the short-term horizon, with no directionality found between ESG and commodities that cannot be explained by volatility. Furthermore, most directionality relationships also disappear when controlling for the volatility transmission between ESG and currency on the short-term horizon. Thus, our findings suggest significantly more integration between ESG and ethical and conventional as bidirectionality overwhelmingly prevails regardless of investment horizon. As previous research has found similarities between ethical and conventional as well as ESG having similar characteristics to commodities as conventional and ethical, we suggest that ESG should be considered as being integrated and having strong similarities with other equities. Thus, it should be treated as being part of the conventional equity asset class. Deviations from bidirectionality could be caused by ESG variable specific heterogeneity. However, despite our rejection of ESG as an independent asset class, it still carries significant potential as it excludes firms with climate-harming practices, thereby helping in combating climate-related as well as social and governance issues the world is facing.

En santé publique, l’étude de la progression d’une maladie chronique et de son mécanisme peut nécessiter la modélisation conjointe de plusieurs marqueurs longitudinaux et leur structure de dépendances. Des approches de modélisation existent dans la littérature pour répondre partiellement aux objectifs de modélisation. Ces approches deviennent rapidement coûteuses et difficiles à utiliser dans certaines maladies complexes à caractère latent, dynamique et multidimensionnel, comme la maladie d’Alzheimer. Dans un tel contexte, l’objectif de cette thèse était de proposer une méthodologie innovante pour modéliser la dynamique de plusieurs processus latents et leurs influences temporelles à des fins d’interprétations causales à partir d’observations répétées de marqueurs continus Gaussiens et non Gaussiens. L’approche proposée, centrée sur des processus latents, définit un modèle structurel pour la trajectoire des processus latents et un modèle d’observation pour lier les marqueurs longitudinaux aux processus qu’ils mesurent. Dans le modèle structurel, défini en temps discret, le niveau initial et le taux de variation des processus spécifiques aux individus sont modélisés par des modèles linéaires à effets mixtes. Le modèle du taux de variation comporte une composante auto-régressive d’ordre 1 qui permet de modéliser l’effet d’un processus sur un autre processus en tenant explicitement compte du temps. Le modèle structurel, tel que défini, bénéficie des mêmes interprétations causales que les modèles à équations différentielles (ODE) de l’approche mécaniste de la causalité tout en évitant les problèmes numériques de ces derniers. Le modèle d’observation utilise des fonctions de lien paramétrées pour que le modèle puisse être appliqué à des marqueurs longitudinaux possiblement non Gaussiens. La méthodologie a été validée par des études de simulations. Cette approche, appliquée à la maladie d’Alzheimer a permis de décrire conjointement la dynamique de l’atrophie de l’hippocampe, du déclin de la mémoire épisodique, du déclin de la fluence verbale et de la perte d’autonomie ainsi que les influences temporelles entre ces dimensions dans plusieurs phases de la maladie à partir des données ADNI. / In public health, the study of the progression of a chronic disease and its mechanisms may require the joint modeling of several longitudinal markers and their dependence structure. Modeling approaches exist in the literature to partially address these modeling objectives. But these approaches become rapidly numerically expensive and difficult to use in some complex diseases involving latent, dynamic and multidimensional aspects, such as in Alzheimer’s disease. The aim of this thesis was to propose an innovative methodology for modeling the dynamics of several latent processes and their temporal influences for the purpose of causal interpretations, from repeated observations of continuous Gaussian and non Gaussian markers. The proposed latent process approach defines a structural model in discrete time for the latent processes trajectories and an observation model to relate longitudinal markers to the process they measure. In the structural model, the initial level and the rate of change of individual-specific processes are modeled by mixedeffect linear models. The rate of change model has a first order auto-regressive component that can model the effect of a process on another process by explicitly accounting for time. The structural model as defined benefits from the same causal interpretations as the models with differential equations (ODE) of the mechanistic approach of the causality while avoiding major numerical problems. The observation model uses parameterized link functions to handle possibly non-Gaussian continuous markers. The consistency of the ML estimators and the accuracy of the inference of the influence structures between the latent processes have been validated by simulation studies. This approach, applied to Alzheimer’s disease, allowed to jointly describe the dynamics of hippocampus atrophy, the decline of episodic memory, the decline of verbal fluency, and loss of autonomy as well as the temporal influences between these dimensions in several stages of Alzheimer’s dementia from the data of the ADNI initiative.

Este trabalho tem como objetivo utilizar medidas de causalidade entre séries temporais de grandezas financeiras para determinar a dependência entre os ativos do mercado e utilizar as medidas obtidas para fazer inferências sobre a dinâmica desses ativos. Essa metodologia define um previsor para os valores das séries que, juntamente com a determinação das distribuições de probabilidades empíricas dos erros desse previsor por meio do método de Kernel, permite a amostragem aleatória de cenários multivariados, com diversas aplicações. Os ativos considerados para os testes de causalidade são o índice Ibovespa, o valor da paridade da moeda dólar-real USDBRL (utilizando suas séries de preços e retornos de preços), além da taxa de juros negociada diariamente (CDI). O uso do Método de Monte Carlo (MMC) é abordado para a precificação de opções de compra europeias (calls) de USDBRL e Ibovespa, e a comparação dos resultados gerados por essa metodologia com valores calculados pela fórmula de Black-Scholes (método mais utilizado no mercado financeiro, atualmente), evidenciando suas vantagens e desvantagens. Conclui-se, com este estudo, que, por meio da metodologia proposta, é possível replicar alguns comportamentos intrínsecos do mercado (como a observação de tendências nas séries de preços devido a dependências implícitas, e a presença de caudas pesadas nas distribuições dos retornos) que são desprezados pela maioria dos modelos paramétricos utilizados hoje, bem como o efeito do uso dessas informações no preço de derivativos. / This paper proposes the use of causality measures applied over time-series of financial values to determine the dependency relations between market assets and a way to use the obtained measures to make inferences about the dynamics of these assets. This methodology defines a predictor for values of the time-series that, by determining the empirical probability distributions of the errors generated by this predictor based on the Kernel method, allows a random sampling of multivariated scenarios with many applications. The assets considered for the causality tests are the Ibovespa index, the dollar-real parity value USDBRL (using their price and price-return series), in addition to the daily traded interest rate (CDI). The use of the Monte Carlo Method (MMC) for the pricing of European call options (USDBRL) and Ibovespa was discussed, in addition to a comparison of the results generated by this methodology with values calculated by the Black-Scholes formula (currently the most used method by finance institutions), showing its advantages and disadvantages. The conclusion is that, based on the proposed methodology, it is possible to replicate some intrinsic market behaviors (such as the existence of trends in price series, due to implicit dependencies, and the presence of fat tails in the distributions of price-returns) that are neglected by most of parametric models, currently, as well as the effect of using this information for pricing derivatives.

A percepção sobre as causas dos acontecimentos faz parte da vida cotidiana. Atribui-se causas aos eventos na busca de entendimento que permita prever, controlar e alterar resultados futuros. Questões como \"Por que não passei na prova?\" e \"Porque fui o primeiro colocado no vestibular?\" conduzem à percepção de uma causa explicativa, seja para o fracasso ou para o sucesso. A Teoria da Atribuição Causal tem sido examinada para compreender e explicar como as pessoas interpretam os determinantes de seu sucesso ou fracasso em situações de desempenho. As causas percebidas estarão relacionadas à percepção de cada indivíduo sobre o evento, o que não implica causalidade real, dado que a ação será efetivada de acordo com a percepção de cada indivíduo sobre o evento. A abordagem teórica utilizada nessa pesquisa foi a Teoria da Atribuição Causal, proposta por Bernard Weiner, no contexto educacional, com foco nas atribuições causais para sucesso e fracasso acadêmicos. Neste cenário, o objetivo principal da pesquisa foi identificar as causas percebidas como explicativas do desempenho acadêmico de estudantes do curso de Ciências Contábeis. Buscou-se também obter evidências e subsidiar a discussão sobre a relação entre o sucesso e o fracasso acadêmico, a modalidade de ensino, a autoestima e o perfil do estudante. Os dados foram coletados por meio de um questionário aplicado aos estudantes de Ciências Contábeis de duas Universidades Federais que oferecem o curso em duas modalidades de ensino (presencial e a distância) e 738 respostas válidas foram obtidas para análise. O questionário foi estruturado em três blocos (I - desempenho e causas percebidas, II - mensuração da autoestima e III - perfil do estudante). Os resultados apresentaram um perfil com idade média dos estudantes de 27,4 anos (34,27 na modalidade EaD e 24,87 na modalidade Presencial). A maioria dos estudantes (83%) exercia atividade remunerada (90% na EaD e 80% na presencial) e as mulheres representaram a maioria dos respondentes (62% na modalidade EaD e 58% na modalidade presencial). As causas internas, especificamente o esforço e a capacidade, foram mais indicadas como explicativas do sucesso acadêmico e as causas externas, especificamente a dificuldade da tarefa, a flexibilidade de horário e a influência negativa do professor, foram as mais indicadas como explicativas do fracasso acadêmico. Como os resultados apontam que os estudantes indicaram com frequência a própria capacidade para explicar o sucesso, pode-se admitir manifestação da tendência autoservidora, que contribui para a manutenção da autoestima, diante da influência positiva na motivação. Entre as causas do sucesso, a capacidade associou-se a um nível mais elevado de autoestima e a causa sorte associou-se a um nível mais baixo de autoestima. Entre as causas do fracasso, a dificuldade da tarefa associou-se ao nível mais baixo de autoestima. Uma análise geral permite observar que os estudantes dedicam pouco tempo aos estudos, atribuem sucesso principalmente a si mesmos e o fracasso a terceiros, e apresentam uma elevada autoestima associada principalmente ao sucesso atribuído à capacidade. Em futuras pesquisas, recomenda-se estudos pilotos, com objetivo de definir outras atribuições causais para elaboração de novos instrumentos de coleta de dados, por meio de abordagem metodológica qualitativa, que possam ampliar os achados e contribuir com a literatura. / The perception about causes of events is part of daily life. Causes are attributed to events in order to be able to make previsions, and control and change future outcomes. Questions like \"why didn\'t I pass the test?\" and \"Why was I the first place in the entrance exam?\" lead to perception of an explanatory cause for failure or success. The Theory of Causal Attribution has been examined to understand and explain how people interpret their success\' or failure\'s determinants in performance situations. The perceived causes will be related to the each individual\'s perception about the event, which does not imply real causality, given that the action takes place according to each individual\'s own perception about the event. The theoretical approach used in this research was the Theory of Causal Attribution, proposed by Bernard Weiner, in the educational context, focusing on causal attributions for academic success and failure. In this context, the main objective of this research was to identify the causes perceived as explanatory of Accounting students\' academic performance. The research also sought to obtain evidence and support to the discussion about the relationship among academic success and failure, the modality of education, self-esteem and students\' profile. The data were collected through a questionnaire administered to Accounting students of two Public Universities offering the Accounting major in two modalities of education (traditional and e-learning), and 738 valid answers were obtained to the analysis. The questionnaire was structured into three parts (I - performance and perceived causes, II - self-esteem measurement and III - students\' profile). The results showed a profile where students are 27.4 years old on average (34.27 in the e-learning modality and 24.87 in the traditional modality). Most students (83%) were working (90% in the e-learning and 80% in the traditional modality) and most respondents (62% in the e-learning and 58% in the traditional modality) were women. The internal causes, specifically effort and ability, were highlighted as explanatory factors for the academic success and the external causes, specifically task difficulty, schedule flexibility and the teacher\'s negative influence, were highlighted as explanatory factors for the academic failure. Since the results show that students often indicate their own ability to explain their success, this indicates a manifestation of the self-serving bias, which contributes to maintaining the self-esteem in the face of motivation\'s positive influence. Among the causes of success, the ability was associated with a higher level of self-esteem and luck was associated with a lower level of self-esteem. Among the causes of failure, the tasks\' difficulty was associated to the lower level of self-esteem. A general analysis allows observing that students spend little time studying, mainly attribute success to themselves and failure to others, and have a high self-esteem mainly associated to the success attributed to capacity. As future researches, pilot studies are recommended in order to set other causal attributions for developing new data collection instruments, through a qualitative approach, that can expand the findings and contribute to the literature.

Les données sous forme de chaîne de symboles sont très variées (ADN, texte, EEG quantifié,…) et ne sont pas toujours modélisables. Une description universelle des chaînes de symboles indépendante des probabilités est donc nécessaire. La complexité de Kolmogorov a été introduite en 1960 pour répondre à cette problématique. Le concept est simple : une chaîne de symboles est complexe quand il n'en existe pas une description courte. La complexité de Kolmogorov est le pendant algorithmique de l’entropie de Shannon et permet de définir la théorie algorithmique de l’information. Cependant, la complexité de Kolmogorov n’est pas calculable en un temps fini ce qui la rend inutilisable en pratique.Les premiers à rendre opérationnelle la complexité de Kolmogorov sont Lempel et Ziv en 1976 qui proposent de restreindre les opérations de la description. Une autre approche est d’utiliser la taille de la chaîne compressée par un compresseur sans perte. Cependant ces deux estimateurs sont mal définis pour le cas conditionnel et le cas joint, il est donc difficile d'étendre la complexité de Lempel-Ziv ou les compresseurs à la théorie algorithmique de l’information.Partant de ce constat, nous introduisons une nouvelle mesure d’information universelle basée sur la complexité de Lempel-Ziv appelée SALZA. L’implémentation et la bonne définition de notre mesure permettent un calcul efficace des grandeurs de la théorie algorithmique de l’information.Les compresseurs sans perte usuels ont été utilisés par Cilibrasi et Vitányi pour former un classifieur universel très populaire : la distance de compression normalisée [NCD]. Dans le cadre de cette application, nous proposons notre propre estimateur, la NSD, et montrons qu’il s’agit d’une semi-distance universelle sur les chaînes de symboles. La NSD surclasse la NCD en s’adaptant naturellement à davantage de diversité des données et en définissant le conditionnement adapté grâce à SALZA.En utilisant les qualités de prédiction universelle de la complexité de Lempel-Ziv, nous explorons ensuite les questions d’inférence de causalité. Dans un premier temps, les conditions algorithmiques de Markov sont rendues calculables grâce à SALZA. Puis en définissant pour la première l’information dirigée algorithmique, nous proposons une interprétation algorithmique de la causalité de Granger algorithmique. Nous montrons, sur des données synthétiques et réelles, la pertinence de notre approche. / Data in the form of strings are varied (DNA, text, quantify EEG) and cannot always be modeled. A universal description of strings, independent of probabilities, is thus necessary. The Kolmogorov complexity was introduced in 1960 to address the issue. The principle is simple: a string is complex if a short description of it does not exist. The Kolmogorov complexity is the counterpart of the Shannon entropy and defines the algorithmic information theory. Yet, the Kolmogorov complexity is not computable in finit time making it unusable in practice.The first ones to make operational the Kolmogorov complexity are Lempel and Ziv in 1976 who proposed to restrain the operations of the description. Another approach uses the size of the compressed string by a lossless data compression algorithm. Yet these two estimators are not well-defined regarding the joint and conditional complexity cases. So, compressors and Lempel-Ziv complexity are not valuable to estimate algorithmic information theory.In the light of this observation, we introduce a new universal information measure based on the Lempel-Ziv complexity called SALZA. The implementation and the good definition of our measure allow computing efficiently values of the algorithmic information theory.Usual lossless compressors have been used by Cilibrasi and Vitányi to define a very popular universal classifier: the normalized compression distance [NCD]. As part of this application, we introduce our own estimator, called the NSD, and we show that the NSD is a universal semi-distance between strings. NSD surpasses NCD because it gets used to a large data set and uses the adapted conditioning with SALZA.Using the accurate universal prediction quality of the Lempel-Ziv complexity, we explore the question of causality inference. At first, we compute the algorithmic causal Markov condition thanks to SALZA. Then we define, for the first time, the algorithmic directed information and based on it we introduce the algorithmic Granger causality. The relevance of our approach is demonstrated on real and synthetic data.

Recent popularization of technologies such as Smartphones, Wearables, Internet of Things, Social Networks and Video streaming increased data creation. Dealing with extensive data sets led the creation of term big data, often defined as when data volume, acquisition rate or representation demands nontraditional approaches to data analysis or requires horizontal scaling for data processing. Analysis is the most important Big Data phase, where it has the objective of extracting meaningful and often hidden information. One example of Big Data hidden information is causality, which can be inferred with Delayed Transfer Entropy (DTE). Despite DTE wide applicability, it has a high demanding processing power which is aggravated with large datasets as those found in big data. This research optimized DTE performance and modified existing code to enable DTE execution on a computer cluster. With big data trend in sight, this results may enable bigger datasets analysis or better statistical evidence. / A recente popularização de tecnologias como Smartphones, Wearables, Internet das Coisas, Redes Sociais e streaming de Video aumentou a criação de dados. A manipulação de grande quantidade de dados levou a criação do termo Big Data, muitas vezes definido como quando o volume, a taxa de aquisição ou a representação dos dados demanda abordagens não tradicionais para analisar ou requer uma escala horizontal para o processamento de dados. A análise é a etapa de Big Data mais importante, tendo como objetivo extrair informações relevantes e às vezes escondidas. Um exemplo de informação escondida é a causalidade, que pode ser inferida utilizando Delayed Transfer Entropy (DTE). Apesar do DTE ter uma grande aplicabilidade, ele possui uma grande demanda computacional, esta última, é agravada devido a grandes bases de dados como as encontradas em Big Data. Essa pesquisa otimizou e modificou o código existente para permitir a execução de DTE em um cluster de computadores. Com a tendência de Big Data em vista, esse resultado pode permitir bancos de dados maiores ou melhores evidências estatísticas.

La présente recherche aborde l’utilisation et l’application de la causalité juridique dans l’analyse, l’extension et la délimitation des conséquences des dommages résultant de la responsabilité civile extracontractuelle subjective. La thèse propose l’individualisation des éléments, favorisant une lecture et approche nouvelles de la causalité pour la détermination d’une zone circonscrite à la réparation de dommages et pour la création d’un modèle théorique de référence pour l’application pratique dans la sélection des dommages indemnisables. Elle a été divisée en deux parties. La première aborde l’origine, la notion et le développement conceptuel de la causalité, jusqu’à l’avènement d’une causalité juridique et l’application des théories de la causalité dans la résolution de problèmes à responsabilité civile ; l’individualisation d’éléments intégrants de la causalité pour la vérification et l’application dans l’analyse de dommages. Dans la deuxième, il est question de l’utilisation de la causalité juridique dans la sélection des dommages indemnisables, de son insertion dans le code civil brésilien en matière d’indemnisation, et de la formulation d’un régime d’imputation ; les cas difficiles, les dommages par ricochet et ceux de pertes d’opportunités, en étant vérifiée la perte d’une opportunité, et la preuve de la causalité, y compris les cas de présomption. / O presente trabalho trata da utilização e aplicação da causalidade jurídica na apuração, extensão e delimitação das consequências dos danos decorrentes da responsabilidade civil extracontratual subjetiva. A tese propõe a individualização de elementos que favoreçam uma nova leitura e abordagem da causalidade com vistas à determinação de uma zona circunscrita à reparação de danos e à criação de um modelo teórico de referência para a aplicação prática na seleção dos danos indenizáveis. Está dividido em duas partes. Na primeira, tratam da origem, da noção e do desenvolvimento conceitual da causalidade, até a assunção de uma causalidade jurídica e da aplicação das teorias da causalidade na solução de problemas de responsabilidade civil; a individualização de elementos integrantes da causalidade para a verificação e a aplicação na apuração de danos. Na segunda, a utilização da causalidade jurídica na seleção dos danos indenizáveis, a sua inserção no Código Civil brasileiro, em matéria de indenização, e a formulação de um regime de imputação; os casos difíceis, os danos por ricochete e os de perda de oportunidades, verificado na perda de uma chance, e a prova da causalidade, incluindo os casos de presunção. / This work approaches the use and application of legal causality in the calculation, extent and delimitation of the consequences of damage from subjective extra-contractual liability. The thesis proposes the individualization of elements that favor a new reading and approach of causality in view of determining a restricted zone to repair damages and the creation of a theoretical model of reference for the practical application in the selection of compensable damage. It is divided into two parts. The first deals with the origin, development and conceptual notion of causality, to the assumption of a legal causality and application of causality theories in solving liability issues; individualization of integral elements of causality for the verification and application in the calculation of damages. Second, the use of legal causality in the selection of compensable damage, its insertion into the Brazilian Civil Code, relating to compensation, and the formulation of a charging system; difficult cases, rebound damages and lost opportunity damages, found with the loss of a chance, and proof of causality, including cases of presumption.

L’hospitalisation représente une étape de vie à laquelle doit faire face une grande majorité des personnes âgées. Cette expérience trop souvent accompagnée d’un déclin physique et psychologique marque pour beaucoup le début de la fin. Basé sur les fondements théoriques de la théorie de l’autodétermination (TAD), ce travail doctoral a deux objectifs. (1) Définir qualitativement le soutien à l’autodétermination dans les services de soins de suite et réadaptation (SSR). (2) Connaître l’impact des orientations de causalité́ des patients sur la perception de soutien ou de contrôle à l’autodétermination et les conséquences motivationnelles et émotionnelles associées. L’étude 1 a montré que les professionnels de santé mettaient en avant une image idéalisée de leur environnement de soins. Les patients (étude 2) ont indiqué de nombreuses contraintes qui atténuent l’effet de cette vision positive. L’étude 3 a démontré que les orientations de causalité du patient étaient une variable qui en interaction avec la perception de l’environnement, n’aboutissait pas aux mêmes conséquences motivationnelles et émotionnelles. De par le rôle déterminant octroyé par la TAD à la motivation, les professionnels de santé se doivent d’être capables de nourrir, entretenir et soutenir la motivation des patients. Une réflexion sur les recommandations pratiques et des axes de recherches futurs seront proposés. / Hospitalization is a life stage that faces a large majority of older people. This experience is often accompanied by both physical and psychological declines and means, for most of older people the beginning of the end. Based on the self-determination theory (SDT), this doctoral dissertation has two objectives. (1) To qualitatively define an autonomy supportive environment in after-care and rehabilitation services (ACRS). (2) To know the impact of causality orientations on patients' perceptions of an autonomy supportive or controlled environment and the associated motivational and emotional consequences. Study 1 showed that healthcare professionals conveyed an idealized image of their work environment and the care they provide. Patients (study 2) indicated many constraints that mitigated the effect of this positive view of an autonomy supportive environment and reduced their ability to experience it. Study 3 demonstrated that patient's causality orientations were a variable which interacted with the perception of ACRS environment, did not induce the same motivational and emotional consequences. Thus, these studies contribute to the self-determination theory in the health setting by showing the determining role of patients’ motivation. It is therefore necessary for healthcare professionals to be able to feed, maintain and support patients’ motivation. In the light of the contribution of the self-determination theory, a reflection about practical recommendations and future lines of research will be proposed.