Relevant bibliographies by topics / Modelli causali

Academic literature on the topic 'Modelli causali'

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Published: 10 March 2023

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Journal articles on the topic "Modelli causali"

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Lorenzi, F. "Breve Storia del Metodo Gemellare 2 - Le Attuali Formulazioni del Metodo." Acta geneticae medicae et gemellologiae: twin research 47, no. 1 (January 1998): 57–71. http://dx.doi.org/10.1017/s0001566000000386.

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Il termine LISREL e l'acronimo di LInear Structural RELationship ed è nato inizialmente come nome di un software messo a punto dallo svedese Karl Joreskog e dai suoi collaboratori nei primi anni '70 per stimare, col metodo della massima verosimiglianza, i coefficienti strutturali dei modelli basati su sistemi di equazioni strutturali.Tali modelli, nella elaborazione tramite il LISREL, rappresentano la sistemazione logica, prima ancora che statistica o computeristica, di tecniche di analisi multivariata le cui prime proposte risalgono all'ininzio del secolo; riconducendo ad un unico modello che ne costituisce una geniale sintesi, approcci ed itinerari scientifici fino ad allora distinti e non comunicanti, quali l'analisi fattoriale, i modelli causali e i modelli di misurazione. In particolare rappresentano in questo momento la più completa e sistematica risposta al problema di operazionalizzare in termini di ricerca e di verifica empirica, nel campo delle scienze sociali, la controversa, ma non per questo meno fondamentale, nozione di causalità. Essi sono quindi la reinterpretazione, sistemazione e soprattutto generalizzazione di quelli che negli anni '60 venivano chiamati i modelli causali e che nella prima metà degli anni '70 avevano conosciuto una notevole popolarità fra i sociologi soprattutto attraverso la tecnica della path analysis.
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Thomae, Helmut, and Horst Kaechele. "La psicoanalisi comparata sulla base di una nuova forma di trascrizione del trattamento. Il caso Amalia X." SETTING, no. 30 (June 2012): 5–44. http://dx.doi.org/10.3280/set2010-030001.

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In questo articolo sosteniamo che č fondamentale, per capire come l'analista applica le sue conoscenze nella situazione analitica, indagare il processo analitico e sviluppare modelli di ricerca per la valutazione delle ipotesi cliniche Partiamo dalla premessa che la teoria dell'analista influenza la tecnica e che l'esame di un'analisi implica un processo interattivo. Ci chiediamo: "Come comprendere il legame inscindibile tra terapia e ricerca?". Sosteniamo che la comprensione che si sviluppa nel corso di una psicoanalisi non č ricerca: perché abbia luogo la ricerca, č necessaria un'esplorazione delle connessioni causali che comprenda le osservazioni e il pensiero dell'analista nel corso dell'analisi, passibili di valutazione da parte di osservatori indipendenti. Viene descritto e dimostrato un modello di ricerca basato sulla trascrizione delle audio-registrazioni e dei commenti annotati dall'analista per fornire a osservatori indipendenti la possibilitŕ di valutare le sue osservazioni e il suo pensiero.
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Kremling, Alexander. "Handlungstheoretischer Interventionismus und Modelle." KRITERION – Journal of Philosophy 1, no. 28 (January 1, 2014): 98–115. http://dx.doi.org/10.1515/krt-2014-012808.

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Abstract The “model argument” against action-theories of causation is a reply to a strategy for avoiding the “argument of unmanipulable causes”. The critics follow the action-theorists in a certain shift of topic - leaving the explication debate towards one of justified assertion of causal claims - and end up at a wrong position concerning the role of practical knowledge for justifying causal claims about events that are not under technical control. Following them the justification would take the form of “analogy arguments“, which can not guarantee the truth of the conclusion in a non-circular way. Contrary to their position I present a reconstruction of causal inference in which practical knowledge about manipulation does play the role of a necessary condition for justifiying causal claims about unmanipulable relations
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De Benedittis, Giuseppe. "La Fisica Quantistica incontra l'ipnosi. Dai fondamenti teorici alla pratica clinica Parte prima." IPNOSI, no. 2 (December 2020): 5–23. http://dx.doi.org/10.3280/ipn2020-002001.

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Il modello dualistico mente-corpo risulta da tempo inadeguato sul piano sia epistemologico che clinico. Per superare la dicotomia tra mente e corpo, viene proposto un nuovo paradigma basato sulla teoria dei sistemi complessi caotici e sulla meccanica quantistica. In questa prospettiva, l'interfaccia mente-corpo rappresenta un sistema caotico, governato dal principio di probabilità, come evidenziato nella fisica subatomica e nella meccanica quantistica, piuttosto che dal principio de-terministico di causalità. Patterns di comportamento caotico possono essere riscontrati nell'attività neuronale e l'applicazione di modelli caotici sembra essere rilevante per la ricerca mente-corpo e il processo di trance. È stata proposta una teoria della coscienza quantistica, in gran parte controversa, poiché la Fisica Quan-tistica si applica al mondo subatomico e non alle macrostrutture, come il cervello. La cognizione quantica è una disciplina emergente che applica il formalismo matematico della teoria quantistica per esplorare e modellare fenomeni cognitivi, come l'elaborazione delle informazioni da parte del cervello umano, supera i limiti e le carenze del dualismo cartesiano e la teoria generale quantistica. Poiché l'ipnosi è uno stato speciale di coscienza, la cognizione quantica si applica al funzionamento cognitivo ipnotico piuttosto che alla struttura ipnotica.
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De Benedittis, Giuseppe. "La Fisica Quantistica incontra l'Ipnosi. Dai fondamenti teorici alla pratica clinica Parte seconda." IPNOSI, no. 1 (July 2021): 5–26. http://dx.doi.org/10.3280/ipn2021-001001.

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Il modello dualistico mente-corpo risulta da tempo inadeguato sul piano sia epistemologico che clinico. Per superare la dicotomia tra mente e corpo, viene pro-posto un nuovo paradigma basato sulla teoria dei sistemi complessi caotici e sulla meccanica quantistica. In questa prospettiva, l'interfàcie Mente/Corpo rappresen-ta un sistema caotico, governato dal principio di probabilità, come evidenziato nel-la fisica subatomica e nella meccanica quantistica, piuttosto che dal principio de-terministico di causalità. Patterns di comportamento caotico possono essere ri-scontrati nell'attività neuronale e l'applicazione di modelli caotici sembra essere rilevante per la ricerca mente-corpo e il processo di trance. È stata proposta una teoria della Coscienza Quantistica, in gran parte controversa, poiché la Fisica Quantistica si applica al mondo subatomico e non alle macrostrutture, come il cervello. La cognizione quantica è una disciplina emergente che applica il formali-smo matematico della teoria quantistica per esplorare e modellare fenomeni co-gnitivi, come l'elaborazione delle informazioni da parte del cervello umano, supera i limiti e le carenze del dualismo cartesiano e la teoria generale quantistica. Poiché l'ipnosi è uno stato speciale di coscienza, la cognizione quantica si applica al fun-zionamento cognitivo ipnotico piuttosto che alla struttura ipnotica.
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Rothman, Philip, Dick van Dijk, and Philip Hans. "MULTIVARIATE STAR ANALYSIS OF MONEY–OUTPUT RELATIONSHIP." Macroeconomic Dynamics 5, no. 4 (September 2001): 506–32. http://dx.doi.org/10.1017/s1365100501023045.

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This paper investigates the potential for nonlinear Granger causality from money to output. Using a standard four-variable linear (subset) vector error-correction model (VECM), we first show that the null hypothesis of linearity can be rejected against the alternative of smooth-transition autoregressive nonlinearity. An interesting result from this stage of the analysis is that the yearly growth rate of money is identified as one of the variables that may govern the switching between regimes. Smooth-transition VECM's (STVECM's) are then used to examine whether there is nonlinear Granger causality in the money–output relationship in the sense that lagged values of money enter the model's output equation as regressors. We evaluate this type of nonlinear Granger causality with both in-sample and out-of-sample analyses. For the in-sample analysis, we compare alternative models using the Akaike information criteria, which can be interpreted as a predictive accuracy test. The results show that allowing for both nonlinearity and for money–output causality leads to considerable improvement in model's in-sample performance. By contrast, the out-of-sample forecasting results do not suggest that money is nonlinearly Granger causal for output. They also show that, according to several criteria, the linear VECM's dominate the STVECM's. However, these forecast improvements seldomly are statistically significant at conventional levels.
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Giarelli, Guido. "Modelli esplicativi delle disuguaglianze di salute: una riflessione sociologica." SALUTE E SOCIETÀ, no. 1 (March 2009): 19–30. http://dx.doi.org/10.3280/ses2009-001003.

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- This essay offers a sociological reading of the different explanatory models of social inequalities in health, through Ardigň's "quadrilateral" scheme, which identifies four types of causal factors of inequalities. Failure to remove such causes generates the so-called paradox of health inequalities, that persist even in the face of overall improvement of health status in post-industrial societies. Keywords: health inequalities, social inequalities, explanatory models, aetiological pathways, social stratification, sociology of health. Parole chiave: disuguaglianze di salute, disuguaglianze sociali, modelli esplicativi, percorsi eziologici, stratificazione sociale, sociologia della salute.
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Dennis, John L., and Aldo Stella. "Priming dei concetti di attivitŕ/passivitŕ: la valorizzazione dell'intenzionalitŕ nella codifica e nella interpretazione di eventi/comportamenti." RICERCHE DI PSICOLOGIA, no. 3 (January 2012): 341–59. http://dx.doi.org/10.3280/rip2010-003002.

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La presente ricerca si occupa del processo di attribuzione causale e, in particolare, della spiegazione di azioni e di comportamenti secondo un modello intenzionale o secondo un modello meccanico. In due studi, che sono stati effettuati utilizzando il priming dei concetti di attivita e di passivita, e emerso che l'innesco del concetto di attivita favorisce interpretazioni piu astratte degli eventi e interpretazioni intenzionali di azioni e comportamenti. Di contro, l'utilizzo del priming della passivita non ha avuto significative influenze sui processi interpretativi e cio ci ha indotto a formulare l'ipotesi che il priming concettuale configura una disposizione asimmetrica del ruolo del soggetto a fronte del ruolo dei fattori meccanici e ambientali.
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Li, Shouwei. "Does Diversification Affect Banking Systemic Risk?" Discrete Dynamics in Nature and Society 2016 (2016): 1–5. http://dx.doi.org/10.1155/2016/2967830.

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This paper contributes to the understanding of the linear and nonlinear causal linkage from diversification to banking systemic risk. Employing data from China, within both linear and nonlinear causality frameworks, we find that diversification does not embody significant predictive power with respect to banking systemic risk.
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Skapinakis, Petros, and Glyn Lewis. "Epidemiology in community psychiatric research: common uses and methodological issues." Epidemiologia e Psichiatria Sociale 10, no. 1 (March 2001): 18–26. http://dx.doi.org/10.1017/s1121189x00008502.

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RIASSUNTOScopo – I principi epidemiologici sono alia base di molte ricerche mediche, in particolare di quelle concernenti la pianificazione e la valutazione dei servizi sanitari, comprese le ricerche di psichiatria sociale e di comunità. Scopo di questo lavoro è quello di effettuare una revisione relativa ad alcune comuni utilizzazioni dell'epidemiologia nelle ricerche di psichiatria di comunità e di discutere alcune questioni metodologiche che si presentano frequentemente nelle ricerche epidemiologiche relative ai setting comunitari. Metodo – Questa è una review della letteratura rilevante e delle ricerche in corso nel Dipartimento di Psicologia Medica dell'Università del Galles, Facoltà di Medicina. Risultati – Tra le varie utilizzazioni dell'epidemiologia nella sanità, quattro sono particolarmente rilevanti nei setting comunitari: l'accertamento dei bisogni di salute mentale della popolazione (vengono descritti quattro approcci: la raccolta di dati di routine, rilevamento dei pazienti esistenti, il rilevamento nella popolazione generale ed i modelli statistici), l'identificazione dei fattori di rischio della malattia, il contributo della prevenzione e la valutazione dell'efficacia clinica degli interventi di cura. Le questioni metodologiche più importanti includono l'inferenza di tipo causale, che in epidemiologia comporta la spiegazione dell'associazione tra esposizione e malattia (caso, bias, fattori confondenti, causalità inversa e causalità), la questione dei fattori confondenti e come tener conto di essi e questioni che sorgono nel contesto di specifici disegni di studio. Conclusioni – L'epidemiologia à divenuta un insieme di metodi utilizzati per rispondere ad un ampio settore di domande cliniche. La ricerca basata sulla popolazione è una parte senziale della ricerca clinica, ma le conoscenze epidemiologiche sono necessarie ai clinici per valutare e interpretare la letteratura scientifica.
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Dissertations / Theses on the topic "Modelli causali"

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FALOTICO, ROSA. "Modelli ad Equazioni Strutturali e Reti Probabilistiche Bayesiane: due approcci a confronto nello studio di relazioni causali." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/19444.

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Causality is a concept extremely important in science, but its definition is quite controversial and its detection is not exempt from problems. There are different approaches to deal with causality. Two of them are the Structural Equation Models (SEMs) and the Probabilistic Bayesian Networks (PBNs). SEMs are confirmative models. Given a causal structure, they test if it is coherent with data. In this context they are estimated using the Partial Least Squares Path Modeling technique in order to obtain the scores of latent variables. PBNs are inductive methods. Their attempt is to extract the causal scheme deriving from data, without presupposing any knowledge. Both models presents advantages and disadvantages regardless of the causality approach they refer to. SEMs are best suited for quantitative data and when there is a solid theoretical knowledge on the subject of analysis. PBNs are preferable for nonlinear analysis or uncertain causal scheme. In the thesis a possible integration of the two methods is proposed in the analysis of data deriving from a satisfaction and customer loyalty survey for “Customer American Satisfaction Index” (ACSI). Results suggest that the SEMs are more suitable than the PBNs and that the integration of the two statistical models is advantageous only in part. This is related to the kind of data, since the ACSI survey is structured for a PLS-PM analysis. Thus, it could be very interesting repeat the comparison for different types of data.
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Brandano, Sergio. "Modelling causal reasoning." Thesis, Queen Mary, University of London, 2009. http://qmro.qmul.ac.uk/xmlui/handle/123456789/625.

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Although human causal reasoning is widely acknowledged as an object of scientific enquiry, there is little consensus on an appropriate measure of progress. Up-to-date evidence of the standard method of research in the field shows that this method has been rejected at the birth of modern science. We describe an instance of the standard scientific method for modelling causal reasoning (causal calculators). The method allows for uniform proofs of three relevant computational properties: correctness of the model with respect to the intended model, full abstraction of the model (function) with respect to the equivalence of reasoning scenarios (input), and formal relations of equivalence and subsumption between models. The method extends and exploits the systematic paradigm [Handbook of Logic in Artificial Intelligence and Logic Programming, volume IV, p. 439-498, Oxford 1995] to fit with our interpretation of it. Using the described method, we present results for some major models, with an updated summary spanning seventy-two years of research in the field.
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Faria, Rodrigo Cristino de. "Modelagem causal da astronomia antiga." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/8/8133/tde-07052015-141417/.

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Esta dissertação tem como objetivo apresentar a Modelagem Causal em História da Ciência (MCHC), e aplicá-la ao período da história da astronomia que vai dos primórdios ao século III AEC. Na primeira parte, exponho o método e discorro sobre algumas de suas implicações filosóficas especialmente aquelas relacionadas com a noção de avanço e historiográficas, ao mesmo tempo em que procuro inseri-lo no panorama da filosofia da ciência. Na segunda parte, que já é uma aplicação da MCHC, apresento uma pequena história da astronomia antiga, mostrando os principais avanços dos egípcios, babilônios e gregos, até Aristarco de Samos. Na última parte, utilizo os conceitos mobilizados na primeira parte e os avanços da segunda para apresentar o modelo causal da astronomia antiga e as conclusões dele derivadas.
This thesis aims at presenting the Causal Modeling of the History of Science (MCHC), and applying it to the period of the history of astronomy comprising its beginning until the 3rd century BCE. In the first part, the method is discussed and some of its philosophical and historiographical implications are analyzed especially those related to the notion of advance. In the second part, an application of MCHC, a short history of ancient, is presented, showing the main advances of the Egyptians, Babylonians, and Greeks, up to Aristarchus of Samos. In the final part, I use the concepts deployed in the first part and the advances discussed in the second part to present a causal model of ancient astronomy and the conclusions therefrom derived.
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Lim, Bobby Ting Chuan. "Causal modelling construction project performance." Thesis, Heriot-Watt University, 1987. http://hdl.handle.net/10399/1011.

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Figueiredo, Garrido M. Isabel. "Causal modelling of evoked brain responses." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/1444174/.

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The aim of this thesis was to test predictive coding as a model of cortical organization and function using a specific brain response, the mismatch negativity (MMN), and a novel tool for connectivity analysis, dynamic causal modelling (DCM). Predictive coding models state that the brain perceives and makes inferences about the world by recursively updating predictions about sensory input. Thus, perception would result from comparing bottom-up input from the environment with top-down predictions. The generation of the MMN, an event- related response elicited by violations in the regularity of a structured auditory sequence, has been discussed extensively in the literature. This thesis discusses the generation of the MMN in the light of predictive coding, in other words, the MMN could reflect prediction error, occurring whenever the current input does not match a previously learnt rule. This interpretation is tested using DCM, a methodological approach which assumes the activity in one cortical area is caused by the activity in another cortical area. In brief, this thesis assesses the validity of DCM, shows the usefulness of DCM in explaining how cortical activity is expressed at the scalp level and exploits the potential of DCM for testing hierarchical models underlying the MMN. The first part of this thesis is concerned with technical issues and establishing the validity of DCM. The second part addresses hierarchical cortical organization in MMN generation, plausible network models or mechanisms underlying the MMN, and finally, the effect of repetition or learning on the connectivity parameters of the causal model.
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Whittaker, Joseph. "A method of reducing model space for dynamic causal modelling." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/a-method-of-reducing-model-space-for-dynamic-causal-modelling(41b3c338-4f1d-4d17-a0b8-58785d6a9f6e).html.

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An increasingly important concept in psychiatric neuroimaging is that of brai nconnectivity. Dynamic Causal Modelling (DCM) has been successfully usedto infer how spatially remote areas of the brain integrate to form functionalnetworks. A potential disadvantage to DCM is the need to predefine a modelbased on a hypothesis about the underlying connectivity. This requirementsmeans the results are dependent on the assumptions about model structure,and important features of the underlying network may be ignored. Here wepresent a method for identifying the model structure in a way t hat discardsthe a priori knowledge that is typically used to constrain model space. Thisallows DCM to be used in a more data-driven way, and allows the optimalmodel within a network of nodes to be identified. The thesis consists of 3studies that together provide a generic framework for a novel approach toDCM and validation that it works, and offers a significant computationaladvantage to traditional DCM.The first study demonstrates that the connectivity within a system of brainregions can be ascertained from inferring the connectivity within smallersystems, which consist of regions taken from the entire system. By analysingthe data in this fashion, we can effectively explore the entire networkstructure space, but estimate a much smaller number of models than wouldbe typical. The second study applies the method to a multicentre dataset andshows that Bayesian Model Selection (BMS) results are reproducible atdifferent centres and across different sessions. The findings show that DCMis robust enough to be used in multicentre studies and that our exploratoryapproach is just as effective as traditional approaches to DCM. The thirdstudy applies the method to a standard psychiatric imaging dataset; animplicit emotional processing face recognition task performed by patientswith major depressive disorder (MDD) vs healthy controls (HC). The MDDpatients perform a follow up scan having being treated with theantidepressant citalopram. The study shows that the developed method canbe used to identify the optimal model structure in order to make inferenceson effective connectivity parameters, and identify differences between patientand control groups, and before and after treatment.
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Mendes, Armando B. "Modelação das elasticidades de quotas de mercado para produtos de grande consumo." Master's thesis, Instituto Superior Técnico, 1997. http://hdl.handle.net/10400.3/2162.

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Dissertação de Mestrado em Investigação Operacional e Engenharia de Sistemas.
As vendas de produtos ao nível da loja são habitualmente modeladas utilizando dados agregados no tempo ou no número de produtos. A agregação permite alisar a série, evitando a interferência de fenómenos como roturas de "stock", frequentes em muitas lojas de grande dimensão. Nesta tese são desenvolvidos modelos de vendas causais a serem utilizados em dados com elevado nível de desagregação e permitindo a análise do comportamento dos diferentes produtos perante roturas, promoções ou alterações de preço. Utiliza-se uma perspectiva descritiva ainda que os modelos possam ser utilizados previsionalmente. Introduz-se o conceito de variável relativizada tanto nos modelos clássicos como nos modelos de atracção. São deduzidas expressões tanto para as elasticidades directas como cruzadas, para cada modelo combinado com cada uma das expressões de relativização. Faz-se uma análise de robustez às expressões verificando-se que além dos modelos de atracção também alguns modelos clássicos conduzem a elasticidades robustas. Os modelos são posteriormente ajustados a dados relativos a uma subfamília com cinco marcas de arroz. Determinam-se e interpretam-se as elasticidades directas e cruzadas do preço nas quotas de mercado. Conclui-se que os modelos clássicos apresentam um maior poder explicativo fornecendo um maior número de parâmetros facilmente interpretáveis como os relacionados com roturas e promoções.
ABSTRACT: Retail product sales are usually modelled using data aggregated over time or by product group. Doing this is necessary in order to reduce bias effects such as stock-outs, very frequent in large stores. The present research develops causal sales models for daily item data, modelling sales price variability, promotions and stock-out substitution. The approach is essentially descriptive, but the developed models can be used for forecasting purposes. The relative marketing variable concept is introduced not only in classical causal models but also in attraction models. Direct and cross elasticity expressions are deduced for every model combined with seven relative variable expressions. These expressions are tested in extreme conditions for robustness. Not only the attraction models are found robust, but same classical models can bring robust elasticity expressions too. The models are then adjusted to any of five products belonging to a rice product group. Direct and cross elasticities are calculated and interpreted. Results from causal classical models and attraction models are compared. Classical models were found to have more descriptive power than attraction models providing several parameters, related with promotions and stock-outs, easier to interpret.
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Fewell, Zoe. "Causal modelling in epidemiology and health services research." Thesis, University of Bristol, 2007. http://hdl.handle.net/1983/f12fb11d-0826-46d6-a5ed-7a87fa582b63.

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Odondi, Lang'O. "Causal modelling of survival data with informative noncompliance." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/causal-modelling-of-survival-data-with-informative-noncompliance(74f40dc0-e5d1-46c0-ab2f-ac42a3425ac7).html.

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Noncompliance to treatment allocation is likely to complicate estimation of causal effects in clinical trials. The ubiquitous nonrandom phenomenon of noncompliance renders per-protocol and as- treated analyses or even simple regression adjustments for noncompliance inadequate for causal inference. For survival data, several specialist methods have been developed when noncompliance is related to risk. The Causal Accelerated Life Model (CALM) allows time-dependent departures from randomized treatment in either arm and relates each observed event time to a potential event time that would have been observed if the control treatment had been given throughout the trial. Alternatively, the structural Proportional Hazards (C-Prophet) model accounts for all-or-nothing noncompliance in the treatment arm only while the CHARM estimator allows time-dependent departures from randomized treatment by considering survival outcome as a sequence of binary outcomes to provide an 'approximate' overall hazard ratio estimate which is adjusted for compliance. The problem of efficacy estimation is compounded for two-active treatment trials (additional noncompliance) where the ITT estimate provides a biased estimator for the true hazard ratio even under homogeneous treatment effects assumption. Using plausible arm-specific predictors of compliance, principal stratification methods can be applied to obtain principal effects for each stratum. The present work applies the above methods to data from the Esprit trials study which was conducted to ascertain whether or not unopposed oestrogen (hormone replacement therapy - HRT) reduced the risk of further cardiac events in postmenopausal women who survive a first myocardial infarction. We use statistically designed simulation studies to evaluate the performance of these methods in terms of bias and 95% confidence interval coverage. We also apply a principal stratification method to adjust for noncompliance in two treatment arms trial originally developed for binary data for survival analysis in terms of causal risk ratio. In a Bayesian framework, we apply the method to Esprit data to account for noncompliance in both treatment arms and estimate principal effects. We apply statistically designed simulation studies to evaluate the performance of the method in terms of bias in the causal effect estimates for each stratum. ITT analysis of the Esprit data showed the effects of taking HRT tablets was not statistically significantly different from placebo for both all cause mortality and myocardial reinfarction outcomes. Average compliance rate for HRT treatment was 43% and compliance rate decreased as the study progressed. CHARM and C-Prophet methods produced similar results but CALM performed best for Esprit: suggesting HRT would reduce risk of death by 50%. Simulation studies comparing the methods suggested that while both C-Prophet and CHARM methods performed equally well in terms of bias, the CALM method performed best in terms of both bias and 95% confidence interval coverage albeit with the largest RMSE. The principal stratification method failed for the Esprit study possibly due to the strong distribution assumption implicit in the method and lack of adequate compliance information in the data which produced large 95% credible intervals for the principal effect estimates. For moderate value of sensitivity parameter, principal stratification results suggested compliance with HRT tablets relative to placebo would reduce risk of mortality by 43% among the most compliant. Simulation studies on performance of this method showed narrower corresponding mean 95% credible intervals corresponding to the the causal risk ratio estimates for this subgroup compared to other strata. However, the results were sensitive to the unknown sensitivity parameter.
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Rees, Stephen Edward. "Causal probabilistic network modelling of lipid and lipoprotein metabolism." Thesis, City University London, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.241469.

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Books on the topic "Modelli causali"

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Quartarone, Jlenia. Causazione e intenzionalità: Modelli di spiegazione causale nella filosofia dell'azione contemporanea. Macerata: Quodlibet, 2008.

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Causazione e intenzionalità: Modelli di spiegazione causale nella filosofia dell'azione contemporanea. Macerata: Quodlibet, 2008.

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Retherford, Robert D. Statistical models for causal analysis. New York: Wiley, 1993.

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Hausman, Daniel M. Causal asymmetries. Cambridge, U.K: Cambridge University Press, 1998.

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M, Blalock Hubert, ed. Causal models in experimental designs. New Brunswick [N.J.]: AldineTransaction, 2007.

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Davis, James Allan. The logic of causal order. Beverly Hills: Sage Publications, 1985.

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The logic of causal order. Beverly Hills: Sage Publications, 1985.

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Horn, Werner, Dipl.-Ing. Dr., ed. Causal AI models: Steps toward applications. New York: Hemisphere Pub. Corp., 1990.

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Linear causal modeling with structural equations. Boca Raton, FL: Chapman & Hall/CRC, 2009.

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Schroeder, Douglas A. Accounting and causal effects: Econometric challenges. New York: Springer, 2010.

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Book chapters on the topic "Modelli causali"

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Sucar, Luis Enrique. "Causal Discovery." In Probabilistic Graphical Models, 307–25. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61943-5_15.

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Edwards, David. "Causal Inference." In Introduction to Graphical Modelling, 219–43. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-0493-0_8.

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Sucar, Luis Enrique. "Graphical Causal Models." In Probabilistic Graphical Models, 237–46. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6699-3_13.

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Sucar, Luis Enrique. "Graphical Causal Models." In Probabilistic Graphical Models, 287–305. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61943-5_14.

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Shafer, Glenn. "Causal Conjecture." In Causal Models and Intelligent Data Management, 17–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58648-4_2.

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Peng, Yun, and James A. Reggia. "Causal Chaining." In Abductive Inference Models for Diagnostic Problem-Solving, 201–26. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4419-8682-5_6.

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Deistler, Manfred, and Wolfgang Scherrer. "Granger Causality." In Time Series Models, 167–73. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13213-1_9.

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Mitchell, Renée J. "Causal Inference." In Twenty-one Mental Models That Can Change Policing, 107–13. New York: Routledge, 2021. http://dx.doi.org/10.4324/9780367481520-24.

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Cox, Louis Anthony. "Causality." In Risk Analysis Foundations, Models, and Methods, 217–300. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0847-2_4.

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Mouchart, Michel, Federica Russo, and Guillaume Wunsch. "Structural Modelling, Exogeneity, and Causality." In Causal Analysis in Population Studies, 59–82. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-9967-0_4.

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Conference papers on the topic "Modelli causali"

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Cavallo, Aurora, Benedetta Di Donato, Rossella Guadagno, and Davide Marino. "Nutrire Roma: il ruolo dell’agricoltura urbana nel fenomeno urbano." In International Conference Virtual City and Territory. Roma: Centre de Política de Sòl i Valoracions, 2014. http://dx.doi.org/10.5821/ctv.8042.

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Scopo di questa nota è di esaminare i caratteri e le dinamiche che connotano l’agricoltura urbana nel caso di Roma. Il contributo sintetizza in chiave evolutiva i fatti stilizzati del rapporto tra città e campagna, successivamente si indaga il contesto produttivo agricolo al fine di proporre una tassonomia dei tipi di agricoltura urbana. Il tentativo che qui si propone è una preliminare lettura dell’agricoltura urbana attraverso un sistema di criteri per la classificazione della distribuzione funzionale e relazionale del primario in aree metropolitane. Tali categorie interpretative tentano di ricostruire le relazioni causali che traducono i modelli produttivi agricoli (caratteristiche strutturali, ordinamenti, forme giuridiche, forme d’uso delle risorse naturali, collocazione), in specifiche forme spaziali e funzionali nella dimensione urbana – fisica e sociale -. Sul piano teorico tale lettura s’inserisce nel paradigma coevolutivo e guarda al paesaggio come il risultato delle interazioni tra il sistema ambientale e l’agire dell’uomo che abita e utilizza il territorio (Marino e Cavallo, 2009). Una sintesi tipologica definitiva sembra ancora un obiettivo da raggiungere, sicuramente questo è il primo passo verso la costruzione di una griglia interpretativa e di un vocabolario tipologico da mettere poi a sistema con i dati morfologici e quelli di uso del suolo. The aim of this paper is to examine the characteristics and the dynamics that characterize urban agriculture in the case of Rome. We summarize in an evolutionary approach the stylized facts of the relationship between town and country, then we investigate the context of agricultural production in order to propose a taxonomy of the types of urban agriculture. The effort proposed here is a preliminary analysis of urban agriculture through a system of criteria for the classification of the distribution of the functional and relational features of agricultural activities in metropolitan areas. These interpretative categories attempt to reconstruct the causal relationships that translate agricultural production models (farms’ data, legal forms, use of natural resources, localization), in specific forms in the spatial and functional urban dimension - physical and social - . On the theoretical level this analysis is embedded in the co-evolutionary paradigm and looks to the landscape as the result of interactions between the environmental system and the action of human who lives and uses the territory (Marino and Cavallo, 2009). This typization ultimately still seems a goal to achieve, this is the first step towards the construction of an interpretative and vocabulary typological then be systematize with the morphological data and those of land use.
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Mahroos, Ashraf M., and Yasser M. Kadah. "Brain causality investigation based on FMRI images time series using dynamic causal modelling augmented by Granger Causality." In 2011 28th National Radio Science Conference (NRSC). IEEE, 2011. http://dx.doi.org/10.1109/nrsc.2011.5873643.

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Perçin, Selçuk, Aykut Karakaya, and Seymur Ağazade. "The Relationship between Export and Innovation in Turkish Manufacturing Industry." In International Conference on Eurasian Economies. Eurasian Economists Association, 2015. http://dx.doi.org/10.36880/c06.01408.

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Posner’s (1961) “technological gap theory” and Vernon’s (1966) “product life cycle theory” predicts that innovation causes market power and facilitates export. In other side export encourages firms and provides more affirmative environment for innovative processes. These theoretical approaches have different predictions regarding the direction of causality between export and innovation. Using Two-Step System Generalized Moments of Method this study investigates causality relationship between export intensity and R&D intensity for the period 2008-2013 in Turkish Manufacturing Industry. Causality relationship was modeled within two different equations and analyzed by Wald Test. First equation models export intensity as function of R&D intensity and real exchange rate. In the second equation R&D intensity estimated as a function of export intensity and Herfindahl-Hirschman Index as competition variable. Causality test results show that there is unidirectional causal relationship from R&D intensity to export intensity. This finding supports the predictions of Posner’s (1961) “technological gap theory” and Vernon’s (1966) “product life cycle theory” related to the innovation and export relationship in Turkish Manufacturing Industry.
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De Medeiros, Adriana Pereira. "Pesquisando Soluções para Desafios em MDE com Kuaba." In Workshop em Modelagem e Simulação de Sistemas Intensivos em Software. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/mssis.2020.12490.

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A pesquisa em Engenharia dirigida por modelos (MDE – Model-Driven Engineering) tem alcançado grande progresso nos últimos anos. No entanto, ainda há desafios técnicos, alguns relacionados à incerteza durante o design de modelos de software, que é causada por muitas alternativas de design, informações incompletas e opiniões conflitantes dos stakeholders. A abordagem Kuaba para design rationale trata as causas dessa incerteza. Este artigo apresenta algumas perspectivas de pesquisa de um projeto em andamento que investiga como Kuaba pode ser utilizada no desenvolvimento de soluções para esses desafios em MDE.
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Ohmori, Nobuyuki, and Tatsunori Mori. "Causal Relation Extraction from Failure Analysis Documents." In Applied Simulation and Modelling. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.777-022.

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Kalimeri, Kyriaki, Bruno Lepri, and Fabio Pianesi. "Causal-modelling of personality traits." In the 2nd international workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1878116.1878130.

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Nagori, M. B., W. Gore Ranjana, and Madhuri Joshi. "Dynamic causal modelling for schizophrenia." In 2011 International Symposium on Humanities, Science and Engineering Research (SHUSER). IEEE, 2011. http://dx.doi.org/10.1109/shuser.2011.6008504.

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Margetts, Rebecca, and Roger F. Ngwompo. "Comparison of Modeling Techniques for a Landing Gear." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-39722.

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A wide range of modeling techniques is available to the engineer. The objective of this paper is to compare some typical modeling techniques for the simulation of a multi-domain mechatronic system. Usual dynamic modeling methods, such as block diagrams and iconic diagrams, can cause problems for the engineer. Differential algebraic equations (DAEs) and algebraic loops can significantly increase simulation times and cause numeric errors. Bond graphs are less common in industry, and are presented here as a method which allows the engineer to easily identify causal loops and elements in differential causality. These can indicate DAEs in the underlying equations. An aircraft landing gear is given as an example of a multi-domain system, and is modeled as a block diagram, an iconic diagram and as a bond graph. The time to construct the model, time to solve and problems faced by the analyst are presented. Bond graphs offer distinct advantages in terms of the ease of implementing algebraic equations and visibility of causality. The time taken to model a system can be significantly reduced and the results appear free from computational errors. Bond graphs are therefore recommended for this type of multi-domain systems analysis.
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Chulaphan, Wanvilai, Chalermpon Jatuporn, Shwu-En Chen, and Pattana Jierwiriyapant. "Causal Impact Price Transmission of the Rice Markets in Thailand." In Modelling, Identification and Control. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.769-051.

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Schöne, René, Johannes Mey, Sebastian Ebert, Sebastian Götz, and Uwe Aßmann. "Incremental causal connection for self-adaptive systems based on relational reference attribute grammars." In MODELS '22: ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3550355.3552460.

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Reports on the topic "Modelli causali"

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Schryver, J. C. Causal models of trip replanning in TravTek. Office of Scientific and Technical Information (OSTI), July 1998. http://dx.doi.org/10.2172/663608.

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Perdigão, Rui A. P., and Julia Hall. Spatiotemporal Causality and Predictability Beyond Recurrence Collapse in Complex Coevolutionary Systems. Meteoceanics, November 2020. http://dx.doi.org/10.46337/201111.

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Causality and Predictability of Complex Systems pose fundamental challenges even under well-defined structural stochastic-dynamic conditions where the laws of motion and system symmetries are known. However, the edifice of complexity can be profoundly transformed by structural-functional coevolution and non-recurrent elusive mechanisms changing the very same invariants of motion that had been taken for granted. This leads to recurrence collapse and memory loss, precluding the ability of traditional stochastic-dynamic and information-theoretic metrics to provide reliable information about the non-recurrent emergence of fundamental new properties absent from the a priori kinematic geometric and statistical features. Unveiling causal mechanisms and eliciting system dynamic predictability under such challenging conditions is not only a fundamental problem in mathematical and statistical physics, but also one of critical importance to dynamic modelling, risk assessment and decision support e.g. regarding non-recurrent critical transitions and extreme events. In order to address these challenges, generalized metrics in non-ergodic information physics are hereby introduced for unveiling elusive dynamics, causality and predictability of complex dynamical systems undergoing far-from-equilibrium structural-functional coevolution. With these methodological developments at hand, hidden dynamic information is hereby brought out and explicitly quantified even beyond post-critical regime collapse, long after statistical information is lost. The added causal insights and operational predictive value are further highlighted by evaluating the new information metrics among statistically independent variables, where traditional techniques therefore find no information links. Notwithstanding the factorability of the distributions associated to the aforementioned independent variables, synergistic and redundant information are found to emerge from microphysical, event-scale codependencies in far-from-equilibrium nonlinear statistical mechanics. The findings are illustrated to shed light onto fundamental causal mechanisms and unveil elusive dynamic predictability of non-recurrent critical transitions and extreme events across multiscale hydro-climatic problems.
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Rehder, Bob. Reasoning, Learning, and Classifying with Uncertain Causal Models. Fort Belvoir, VA: Defense Technical Information Center, November 2012. http://dx.doi.org/10.21236/ada578263.

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Pearl, Judea. Linear Models: A Useful Microscope for Causal Analysis. Fort Belvoir, VA: Defense Technical Information Center, February 2013. http://dx.doi.org/10.21236/ada579021.

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Athey, Susan, Mohsen Bayati, Nikolay Doudchenko, Guido Imbens, and Khashayar Khosravi. Matrix Completion Methods for Causal Panel Data Models. Cambridge, MA: National Bureau of Economic Research, October 2018. http://dx.doi.org/10.3386/w25132.

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Arkhangelsky, Dmitry, and Guido Imbens. Double-Robust Identification for Causal Panel Data Models. Cambridge, MA: National Bureau of Economic Research, January 2021. http://dx.doi.org/10.3386/w28364.

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Abadie, Alberto. Semiparametric Estimation of Instrumental Variable Models for Causal Effects. Cambridge, MA: National Bureau of Economic Research, September 2000. http://dx.doi.org/10.3386/t0260.

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Reiser, Brian J. Causal Models in the Acquisition and Instruction of Programming Skills. Fort Belvoir, VA: Defense Technical Information Center, March 1992. http://dx.doi.org/10.21236/ada248761.

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Tenenbaum, Joshua B., Leslie P. Kaelbling, Michael L. Littman, and David Wingate. Acquiring and Exploiting Rich Causal Models for Robust Decision Making. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada566219.

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Reiser, Brian J. Causal Models in the Acquisition and Instruction of Programming Skills. Fort Belvoir, VA: Defense Technical Information Center, October 1991. http://dx.doi.org/10.21236/ada242899.

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