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Calculating control variables with age at onset data to adjust for conditions prior to exposureHöfler, Michael, Brueck, Tanja, Lieb, Roselind, Wittchen, Hans-Ulrich 20 February 2013 (has links) (PDF)
Background: When assessing the association between a factor X and a subsequent outcome Y in observational studies, the question that arises is what are the variables to adjust for to reduce bias due to confounding for causal inference on the effect of X on Y. Disregarding such factors is often a source of overestimation because these variables may affect both X and Y. On the other hand, adjustment for such variables can also be a source of underestimation because such variables may be the causal consequence of X and part of the mechanism that leads from X to Y.
Methods: In this paper, we present a simple method to compute control variables in the presence of age at onset data on both X and a set of other variables. Using these age at onset data, control variables are computed that adjust only for conditions that occur prior to X. This strategy can be used in prospective as well as in survival analysis. Our method is motivated by an argument based on the counterfactual model of a causal effect.
Results: The procedure is exemplified by examining of the relation between panic attack and the subsequent incidence of MDD.
Conclusions: The results reveal that the adjustment for all other variables, irrespective of their temporal relation to X, can yield a false negative result (despite unconsidered confounders and other sources of bias).
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Feature Selection under Multicollinearity & Causal Inference on Time SeriesBhattacharya, Indranil January 2017 (has links) (PDF)
In this work, we study and extend algorithms for Sparse Regression and Causal Inference problems. Both the problems are fundamental in the area of Data Science.
The goal of regression problem is to nd out the \best" relationship between an output variable and input variables, given samples of the input and output values. We consider sparse regression under a high-dimensional linear model with strongly correlated variables, situations which cannot be handled well using many existing model selection algorithms. We study the performance of the popular feature selection algorithms such as LASSO, Elastic Net, BoLasso, Clustered Lasso as well as Projected Gradient Descent algorithms under this setting in terms of their running time, stability and consistency in recovering the true support. We also propose a new feature selection algorithm, BoPGD, which cluster the features rst based on their sample correlation and do subsequent sparse estimation using a bootstrapped variant of the projected gradient descent method with projection on the non-convex L0 ball. We attempt to characterize the efficiency and consistency of our algorithm by performing a host of experiments on both synthetic and real world datasets.
Discovering causal relationships, beyond mere correlation, is widely recognized as a fundamental problem. The Causal Inference problems use observations to infer the underlying causal structure of the data generating process. The input to these problems is either a multivariate time series or i.i.d sequences and the output is a Feature Causal Graph where the nodes correspond to the variables and edges capture the direction of causality. For high dimensional datasets, determining the causal relationships becomes a challenging task because of the curse of dimensionality. Graphical modeling of temporal data based on the concept of \Granger Causality" has gained much attention in this context. The blend of Granger methods along with model selection techniques, such as LASSO, enables efficient discovery of a \sparse" sub-set of causal variables in high dimensional settings. However, these temporal causal methods use an input parameter, L, the maximum time lag. This parameter is the maximum gap in time between the occurrence of the output phenomenon and the causal input stimulus. How-ever, in many situations of interest, the maximum time lag is not known, and indeed, finding the range of causal e ects is an important problem. In this work, we propose and evaluate a data-driven and computationally efficient method for Granger causality inference in the Vector Auto Regressive (VAR) model without foreknowledge of the maximum time lag. We present two algorithms Lasso Granger++ and Group Lasso Granger++ which not only constructs the
hypothesis feature causal graph, but also simultaneously estimates a value of maxlag (L) for each variable by balancing the trade-o between \goodness of t" and \model complexity".
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Efeito da eleição do candidato da oposição sobre a apresentação e aprovação de projetos do prefeito na CâmaraScott Filho, Renato Alexandre 05 February 2015 (has links)
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Previous issue date: 2015-02-05 / This study aims to analyze the effect of a challenger winning the municipal elections on the presentation and approval by the city council of projects proposed by the then elected mayor, on the subsequent year. This subject relates to the advantage citizens take when they choose the alternation of power option instead of the reelection one, as well as to the relation between the new chief of the executive and the city councilmen. A bound is then established with an electoral literature that excels at the analysis of factors leading to the reelection of a politician, but that may well be deepened on the examination of the future consequences of the alternation of power. In a similar way, a contribution is made by the provision of an econometrical toolset to the theoretical analyses of executive-legislative relations in the local level. The employed methodology is based on the application of the regression discontinuity design technique (or RDD) to data collected from the Brazilian Supreme Electoral Court and from a 2005’s legislative census. Following the data examination, and with moderate significance, it is argued that when the challenger is elected mayor he tends to propose less projects in the first year of his term (as opposed to the incumbent mayor), but these projects, on the other hand, get a proportionally more favorable reception by the city council. / Este trabalho tem por objetivo analisar o efeito de se eleger um candidato da oposição em eleições municipais sobre a apresentação e aprovação de projetos do prefeito então eleito na câmara de vereadores, no ano subsequente. Tal questão diz respeito à vantagem, para os munícipes, de optar pela alternância de poder em detrimento da reeleição, bem como à relação entre o novo representante do executivo e os vereadores. Dialoga-se, assim, com uma literatura político-eleitoral pródiga na análise dos fatores que favorecem a reeleição, mas que pouco se debruçou sobre as consequências futuras da alternância de poder. Igualmente, contribui-se com um embasamento econométrico para as discussões fomentadas a nível teórico a respeito das relações entre os poderes no município. A metodologia empregada se baseia na técnica de regressões descontínuas ou RDD para tratamento quase-experimental dos dados coletados no TSE e no Censo Legislativo de 2005. A partir da análise dos dados, e com significância moderada, argumenta-se que quando o prefeito eleito é da oposição ele tende a apresentar menos projetos à câmara em seu primeiro ano de mandato, mas recebe acolhida proporcionalmente mais favorável por parte dos vereadores.
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Utilisation des antifongiques chez le patient non neutropénique en réanimation / Antifungal use on non neutropenic patients in Intensive Care UnitBailly, Sébastien 15 October 2015 (has links)
Les levures du genre Candida figurent parmi les pathogènes majeurs isolés chez les patients en soins intensifs et sont responsables d'infections systémiques : les candidoses invasives. Le retard et le manque de fiabilité du diagnostic sont susceptibles d'aggraver l'état du patient et d'augmenter le risque de décès à court terme. Pour respecter les objectifs de traitement, les experts recommandent de traiter le plus précocement possible les patients à haut risque de candidose invasive. Cette attitude permet de proposer un traitement précoce aux malades atteints, mais peut entraîner un traitement inutile et coûteux et favoriser l'émergence de souches de moindre sensibilité aux antifongiques utilisés.Ce travail applique des méthodes statistiques modernes à des données observationnelles longitudinales. Il étudie l'impact des traitements antifongiques systémiques sur la répartition des quatre principales espèces de Candida dans les différents prélèvements de patients en réanimation médicale, sur leur sensibilité à ces antifongiques, sur le diagnostic des candidémies ainsi que sur le pronostic des patients. Les analyses de séries de données temporelles à l'aide de modèles ARIMA (moyenne mobile autorégressive intégrée) ont confirmé l'impact négatif de l'utilisation des antifongiques sur la sensibilité des principales espèces de Candida ainsi que la modification de leur répartition sur une période de dix ans. L'utilisation de modèles hiérarchiques sur données répétées a montré que le traitement influence négativement la détection des levures et augmente le délai de positivité des hémocultures dans le diagnostic des candidémies. Enfin, l'utilisation des méthodes d'inférence causale a montré qu'un traitement antifongique préventif n'a pas d'impact sur le pronostic des patients non neutropéniques, non transplantés et qu'il est possible de commencer une désescalade précoce du traitement antifongique entre le premier et le cinquième jour après son initiation sans aggraver le pronostic. / Candida species are among the main pathogens isolated from patients in intensive care units (ICUs) and are responsible for a serious systemic infection: invasive candidiasis. A late and unreliable diagnosis of invasive candidiasis aggravates the patient's status and increases the risk of short-term death. The current guidelines recommend an early treatment of patients with high risks of invasive candidiasis, even in absence of documented fungal infection. However, increased antifungal drug consumption is correlated with increased costs and the emergence of drug resistance whereas there is yet no consensus about the benefits of the probabilistic antifungal treatment.The present work used modern statistical methods on longitudinal observational data. It investigated the impact of systemic antifungal treatment (SAT) on the distribution of the four Candida species most frequently isolated from ICU patients', their susceptibilities to SATs, the diagnosis of candidemia, and the prognosis of ICU patients. The use of autoregressive integrated moving average (ARIMA) models for time series confirmed the negative impact of SAT use on the susceptibilities of the four Candida species and on their relative distribution over a ten-year period. Hierarchical models for repeated measures showed that SAT has a negative impact on the diagnosis of candidemia: it decreases the rate of positive blood cultures and increases the time to positivity of these cultures. Finally, the use of causal inference models showed that early SAT has no impact on non-neutropenic, non-transplanted patient prognosis and that SAT de-escalation within 5 days after its initiation in critically ill patients is safe and does not influence the prognosis.
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Contrôle, agentivité et apprentissage par renforcement / Control, agency and reinforcement learning in human decision-makingThéro, Héloïse 26 September 2018 (has links)
Le sentiment d’agentivité est défini comme le sentiment de contrôler nos actions, et à travers elles, les évènements du monde extérieur. Cet ensemble phénoménologique dépend de notre capacité d’apprendre les contingences entre nos actions et leurs résultats, et un algorithme classique pour modéliser cela vient du domaine de l’apprentissage par renforcement. Dans cette thèse, nous avons utilisé l’approche de modélisation cognitive pour étudier l’interaction entre agentivité et apprentissage par renforcement. Tout d’abord, les participants réalisant une tâche d’apprentissage par renforcement tendent à avoir plus d’agentivité. Cet effet est logique, étant donné que l’apprentissage par renforcement consiste à associer une action volontaire et sa conséquence. Mais nous avons aussi découvert que l’agentivité influence l’apprentissage de deux manières. Le mode par défaut pour apprendre des contingences action-conséquence est que nos actions ont toujours un pouvoir causal. De plus, simplement choisir une action change l’apprentissage de sa conséquence. En conclusion, l’agentivité et l’apprentissage par renforcement, deux piliers de la psychologie humaine, sont fortement liés. Contrairement à des ordinateurs, les humains veulent être en contrôle, et faire les bons choix, ce qui biaise notre aquisition d’information. / Sense of agency or subjective control can be defined by the feeling that we control our actions, and through them effects in the outside world. This cluster of experiences depend on the ability to learn action-outcome contingencies and a more classical algorithm to model this originates in the field of human reinforcementlearning. In this PhD thesis, we used the cognitive modeling approach to investigate further the interaction between perceived control and reinforcement learning. First, we saw that participants undergoing a reinforcement-learning task experienced higher agency; this influence of reinforcement learning on agency comes as no surprise, because reinforcement learning relies on linking a voluntary action and its outcome. But our results also suggest that agency influences reinforcement learning in two ways. We found that people learn actionoutcome contingencies based on a default assumption: their actions make a difference to the world. Finally, we also found that the mere fact of choosing freely shapes the learning processes following that decision. Our general conclusion is that agency and reinforcement learning, two fundamental fields of human psychology, are deeply intertwined. Contrary to machines, humans do care about being in control, or about making the right choice, and this results in integrating information in a one-sided way.
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Calculating control variables with age at onset data to adjust for conditions prior to exposureHöfler, Michael, Brueck, Tanja, Lieb, Roselind, Wittchen, Hans-Ulrich January 2005 (has links)
Background: When assessing the association between a factor X and a subsequent outcome Y in observational studies, the question that arises is what are the variables to adjust for to reduce bias due to confounding for causal inference on the effect of X on Y. Disregarding such factors is often a source of overestimation because these variables may affect both X and Y. On the other hand, adjustment for such variables can also be a source of underestimation because such variables may be the causal consequence of X and part of the mechanism that leads from X to Y.
Methods: In this paper, we present a simple method to compute control variables in the presence of age at onset data on both X and a set of other variables. Using these age at onset data, control variables are computed that adjust only for conditions that occur prior to X. This strategy can be used in prospective as well as in survival analysis. Our method is motivated by an argument based on the counterfactual model of a causal effect.
Results: The procedure is exemplified by examining of the relation between panic attack and the subsequent incidence of MDD.
Conclusions: The results reveal that the adjustment for all other variables, irrespective of their temporal relation to X, can yield a false negative result (despite unconsidered confounders and other sources of bias).
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Apprentissage de modèles causaux par réseaux de neurones artificielsBrouillard, Philippe 07 1900 (has links)
Dans ce mémoire par articles, nous nous intéressons à l’apprentissage de modèles causaux à
partir de données. L’intérêt de cette entreprise est d’obtenir une meilleure compréhension
des données et de pouvoir prédire l’effet qu’aura un changement sur certaines variables d’un
système étudié. Comme la découverte de liens causaux est fondamentale en sciences, les
méthodes permettant l’apprentissage de modèles causaux peuvent avoir des applications
dans une pléthore de domaines scientifiques, dont la génomique, la biologie et l’économie.
Nous présentons deux nouvelles méthodes qui ont la particularité d’être des méthodes
non-linéaires d’apprentissage de modèles causaux qui sont posées sous forme d’un problème
d’optimisation continue sous contrainte. Auparavant, les méthodes d’apprentissage de mo-
dèles causaux abordaient le problème de recherche de graphes en utilisant des stratégies de
recherche voraces. Récemment, l’introduction d’une contrainte d’acyclicité a permis d’abor-
der le problème différemment.
Dans un premier article, nous présentons une de ces méthodes: GraN-DAG. Sous cer-
taines hypothèses, GraN-DAG permet d’apprendre des graphes causaux à partir de données
observationnelles. Depuis la publication du premier article, plusieurs méthodes alternatives
ont été proposées par la communauté pour apprendre des graphes causaux en posant aussi
le problème sous forme d’optimisation continue avec contrainte. Cependant, aucune de ces
méthodes ne supportent les données interventionnelles. Pourtant, les interventions réduisent
le problème d’identifiabilité et permettent donc l’utilisation d’architectures neuronales plus
expressives. Dans le second article, nous présentons une autre méthode, DCDI, qui a la
particularité de pouvoir utiliser des données avec différents types d’interventions. Comme
le problème d’identifiabilité est moins important, une des deux instanciations de DCDI est
un approximateur de densité universel. Pour les deux méthodes proposées, nous montrons
que ces méthodes ont de très bonnes performances sur des données synthétiques et réelles
comparativement aux méthodes traditionelles. / In this thesis by articles, we study the learning of causal models from data. The goal of
this entreprise is to gain a better understanding of data and to be able to predict the effect
of a change on some variables of a given system. Since discovering causal relationships is
fundamental in science, causal structure learning methods have applications in many fields
that range from genomics, biology, and economy.
We present two new methods that have the particularity of being non-linear methods
learning causal models casted as a continuous optimization problem subject to a constraint.
Previously, causal strutural methods addressed this search problem by using greedy search
heuristics. Recently, a new continuous acyclity constraint has allowed to address the problem
differently.
In the first article, we present one of these non-linear method: GraN-DAG. Under some
assumptions, GraN-DAG can learn a causal graph from observational data. Since the publi-
cation of this first article, several alternatives methods have been proposed by the community
by using the same continuous-constrained optimization formulation. However, none of these
methods support interventional data. Nevertheless, interventions reduce the identifiability
problem and allow the use of more expressive neural architectures. In the second article,
we present another method, DCDI, that has the particularity to leverage data with several
kinds of interventions. Since the identifiabiliy issue is less severe, one of the two instantia-
tions of DCDI is a universal density approximator. For both methods, we show that these
methods have really good performances on synthetic and real-world tasks comparatively to
other classical methods.
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A Life Course Perspective on Social Connectedness and Adult Health.pdfElizabeth A Teas (15315958) 19 April 2023 (has links)
<p>Functional impairment is increasingly prevalent among middle-aged and older adults, with 2 in 5 adults over the age of 65 having some form of disability, the majority being limitations on mobility. Many older adults are able to maintain functional capacity well into later life, but the factors that contribute to high levels of function and the mechanisms by which they operate are unclear, although prior work has demonstrated the importance of social relationships for health. Guided by principles from the life course perspective and perspectives on social connectedness, this dissertation examined the role of social connectedness across the life course as a predictor of functional capacity in adulthood. I used existing longitudinal data from the national Midlife in the United States (MIDUS) study to pursue three central aims.</p>
<p><br></p>
<p>First, Paper 1 compared theoretical and data-driven approaches to classifying life course relationships, including multiple dimensions of social connectedness at different time points across the life course. Results showed that the data-driven approach (i.e., latent profile analysis) was a stronger predictor of functional limitations than the theoretical approach and revealed relationship trajectories consistent with life course cumulative processes. Second, using the profiles obtained from Paper 1, Paper 2 probed the association between life-course social connectedness and functional limitations by examining the potential mediating role of candidate biological and behavioral mechanisms, and moderation by socioeconomic status (SES). Paper 2 findings suggested that observed differences in later-life functional limitations based on life-course social connectedness can be at least partially explained by physical activity, but do not vary by SES. Contrary to hypotheses, inflammation was not a significant mediator. Third, Paper 3 used monozygotic twin data and within-family analyses to sharpen the focus on potential causal associations between life-course social connectedness and adult functional status. Results suggested that the association is likely driven by genetic and/or shared environmental influences. </p>
<p><br></p>
<p>Taken together, these results add to our understanding of social connectedness and health and address important gaps in the literature. These findings are used to generate theory- and intervention-relevant insights into the successful maintenance of health, independence, and function across the lifespan.</p>
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Détection de l’invalidité et estimation d’un effet causal en présence d’instruments invalides dans un contexte de randomisation mendélienneBoucher-Roy, David 08 1900 (has links)
La randomisation mendélienne est une méthode d’instrumentation utilisant des instruments
de nature génétique afin d’estimer, via par exemple la régression des moindres
carrés en deux étapes, une relation de causalité entre un facteur d’exposition et une réponse
lorsque celle-ci est confondue par une ou plusieurs variables de confusion non mesurées. La
randomisation mendélienne est en mesure de gérer le biais de confusion à condition que les
instruments utilisés soient valides, c’est-à-dire qu’ils respectent trois hypothèses clés. On
peut généralement se convaincre que deux des trois hypothèses sont satisfaites alors qu’un
phénomène génétique, la pléiotropie, peut parfois rendre la troisième hypothèse invalide.
En présence d’invalidité, l’estimation de l’effet causal de l’exposition sur la réponse peut
être sévèrement biaisée. Afin d’évaluer la potentielle présence d’invalidité lorsqu’un seul
instrument est utilisé, Glymour et al. (2012) ont proposé une méthode qu’on dénomme ici
l’approche de la différence simple qui utilise le signe de la différence entre l’estimateur des
moindres carrés ordinaires de la réponse sur l’exposition et l’estimateur des moindres carrés
en deux étapes calculé à partir de l’instrument pour juger de l’invalidité de l’instrument. Ce
mémoire introduit trois méthodes qui s’inspirent de cette approche, mais qui sont applicables
à la randomisation mendélienne à instruments multiples. D’abord, on introduit l’approche
de la différence globale, une simple généralisation de l’approche de la différence simple au cas
des instruments multiples qui a comme objectif de détecter si un ou plusieurs instruments
utilisés sont invalides. Ensuite, on introduit les approches des différences individuelles et des
différences groupées, deux méthodes qui généralisent les outils de détection de l’invalidité
de l’approche de la différence simple afin d’identifier des instruments potentiellement
problématiques et proposent une nouvelle estimation de l’effet causal de l’exposition sur la
réponse. L’évaluation des méthodes passe par une étude théorique de l’impact de l’invalidité
sur la convergence des estimateurs des moindres carrés ordinaires et des moindres carrés
en deux étapes et une simulation qui compare la précision des estimateurs résultant des
différentes méthodes et leur capacité à détecter l’invalidité des instruments. / Mendelian randomization is an instrumentation method that uses genetic instruments
to estimate, via two-stage least squares regression for example, a causal relationship
between an exposure and an outcome when the relationship is confounded by one or more
unmeasured confounders. Mendelian randomization can handle confounding bias provided
that the instruments are valid, i.e., that they meet three key assumptions. While two of
the three assumptions can usually be satisfied, the third assumption is often invalidated
by a genetic phenomenon called pleiotropy. In the presence of invalid instruments, the
estimate of the causal effect of exposure on the outcome may be severely biased. To assess
the potential presence of an invalid instrument in single-instrument studies, Glymour et
al. (2012) proposed a method, hereinafter referred to as the simple difference approach,
which uses the sign of the difference between the ordinary least squares estimator of the
outcome on the exposure and the two-stage least squares estimator calculated using the
instrument. Based on this approach, we introduce three methods applicable to Mendelian
randomization with multiple instruments. The first method is the global difference approach
and corresponds to a simple generalization of the simple difference approach to the case of
multiple instruments that aims to detect whether one or more instruments are invalid. Next,
we introduce the individual differences and the grouped differences approaches, two methods
that generalize the simple difference approach to identify potentially invalid instruments
and provide new estimates of the causal effect of the exposure on the outcome. The methods
are evaluated using a theoretical investigation of the impact that invalid instruments have
on the convergence of the ordinary least squares and two-stage least squares estimators as
well as with a simulation study that compares the accuracy of the respective estimators and
the ability of the corresponding methods to detect invalid instruments.
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[en] CAUSAL REASONING AND INDUCTION IN DAVID HUME / [pt] RACIOCÍNIO CAUSAL E INFERÊNCIA INDUTIVA NO PENSAMENTO DE DAVID HUMECARLOS JACINTO NASCIMENTO MOTTA 25 November 2005 (has links)
[pt] Esta dissertação tem por objetivo apresentar os resultados
da pesquisa de
mestrado em que se procurou evidenciar algumas
características da relação de
David Hume com a indução. Segundo a interpretação
corrente, Hume é o
responsável por mostrar que nossa razão não é capaz de
justificar qualquer um
de nossos raciocínios indutivos. O problema de Hume também
se caracteriza
por ser um problema acerca da racionalidade da ciência,
pois se seu método
principal, a indução, não pode receber suporte racional,
parece lícito afirmar
que o resultado de uma inferência indutiva é irracional. A
fim de delinear o
campo exato em que se insere a crítica humeana, este texto
irá mostrar como
Hume apresenta suas teorias acerca do raciocínio causal em
seu Tratado da
natureza humana, traçar as características exatas do
raciocínio causal de
Hume e confrontá-las com as formas de interpretação
presentes em alguns de
seus principais comentadores. Procuramos tornar claras as
falhas
apresentadas nestas interpretações. Em seguida trataremos
de discutir
algumas das mais celebradas interpretações da filosofia de
Hume, centrando
nossa análise nos textos de Mackie, Beauchamp e Mappes. O
capítulo final
tem por objetivo mostrar as características racionais que
podem ser atribuídas
aos raciocínios causais humeanos, salientando o caráter
particular de suas
inferências. Finalizando, mostraremos como a origem do
princípio da cópia
pode ser um exemplo do uso de inferências indutivas por
parte de Hume, o que
nos leva a considerações heterodoxas a respeito de sua
visão a respeito da
racionalidade. / [en] The aim of this work is to present the results of my
master´s degree research,
which tried to show some of the characteristics of David
Hume´s approach to
induction. According to the standard interpretation, Hume
is responsible for
showing that our reason is not able to justify any of our
inductive reasonings.
Hume´s problem also characterizes itself by being a
problem about the
rationality of science, for, since his main method,
induction, cannot receive a
rational foundation, it seems licit to assert that the
result of any inductive
inference is irrational. In order to precisely describe
the Humean criticism I am
going to show how Hume presents his theories concerning
causal reasoning in
this A Treatise of Human Nature, define the exact
characteristics of causal
reasoning according to him, and compare this analysis to
those by some of his
main critics. We shall try to bring to light the proposed
inadequacy of the latter.
Next we will discuss some of the most celebrated
interpretations of Hume´s
philosophy, specially those by of Mackie, Beauchamp and
Mappes. The final
chapter aims at showing the rational characteristics that
can be assigned to
Humean causal reasoning emphasizing the particular
character of his
inferences. Finally, we show how the origin of the copy
principle can be an
instance of the use of inductive inferences by Hume, which
allows us to risk
some heterodox hypotheses concerning his view of
rationality.
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