Causal Abstraction with Soft Interventions

Causal abstraction provides a theory describing how several causal models can represent the same system at different levels of detail. Existing theoretical proposals limit the analysis of abstract models to "hard" interventions fixing causal variables to be constant values. In this work, we extend causal abstraction to "soft" interventions, which assign possibly non-constant functions to variables without adding new causal connections. Specifically, (i) we generalize $\tau$-abstraction from Beckers and Halpern (2019) to soft interventions, (ii) we propose a further definition of soft abstraction to ensure a unique map $\omega$ between soft interventions, and (iii) we prove that our constructive definition of soft abstraction guarantees the intervention map $\omega$ has a specific and necessary explicit form

Inter-individual variability in psychological outcomes of supervised exercise in adults with Type 2 Diabetes

Exercise is a key component in the management of Type 2 Diabetes Mellitus (T2DM), however despite the strong evidence of its protective effects, a majority of the population with this diagnosis remains inactive and those who start an exercise program are not willing to train themselves over the long-term. Self-ef cacy and perceived stress are related to barriers to exercise in T2DM, therefore the aim of this longitudinal study is to investigate variations across time and individual differences in both variables as effects of a supervised exercise training (6 months) in a small sample of persons diagnosed with T2DM. Results show a general decline in the mean values of self-ef cacy and perceived stress at 6 months and a high individual variability in both variables. These results support the need to develop customized pro- grams of exercise in T2DM that take into account different phases of the exercise process and individual variability.El ejercicio es un componente clave en la prevención y el tratamiento de Diabetes Mellitus Tipo 2 (DMT2); sin embargo, a pesar de la fuerte evidencia de sus efectos protectores, la mayoría de las personas con este diagnóstico permanece inactiva y aquellos que comienzan un programa de ejercicio no están dispuestos a entrenar a largo plazo. La autoefcacia y la percepción de estrés se relacionan con las barreras para realizar ejercicio en pacientes T2DM; por lo tanto, el objetivo de este estudio longitudinal consiste en investigar las variaciones a través del tiempo y las diferencias individuales en ambas variables, como efectos de un entrenamiento de ejercicio supervisado (6 meses), en una muestra pequeña de pacientes diagnosticados con TD2M. Los resultados muestran una disminución general de los valores promedio de la autoefcacia y del estrés percibido a los 6 meses y una alta variabilidad individual en ambas variables. Estos resultados apoyan la necesidad de desarrollar programas personalizados de ejercicio en pacientes T2DM con el objetivo de considerar las diferentes fases del proceso de ejercicio y de la variabilidad individual

Mixed effects of a six-month supervised exercise program in overweight and moderately obese adults with Type 2 diabetes mellitus

Type 2 Diabetes Mellitus (T2DM) is often associated with overweight or obesity. Clinical practice guidelines recommend that people with T2DM should regularly perform aerobic and resistance exercise and reduce the amount of time spent sitting. However, most adults with T2DM remain inactive and those who start a program are not willing to maintain exercise for the long run. To evaluate the relationship between supervised exercise, glycemic control, fitness and potential body image a longitudinal study with intervention was conducted. Twenty-three T2DM adults were assessed on Body Mass Index, glycosylated haemoglobin A1c (HbA1c), Fitness Index (FI) and Potential Body Image (PBI) at baseline and after completing a six-month supervised exercise program. BMI and Fitness Index were modified by exercise. No group differences were found on HbA1c and PBI. However, significant individual differences in BPI were detected by means of mixed-effects models. A six-month exercise program can affect some biological and clinical parameters as BMI and Fitness Index. High inter-individual variability was observed in PBI. Mixed-effects models should be preferred to the traditional ANOVA's and personalized supervised intervention should be implemented for long-term maintenance of exercise

Successful Treatment of Chronic Mucocutaneous Candidiasis Caused by Azole-Resistant Candida albicans with Posaconazole

Refractory or recurrent infections of skin, nails, and the mucous membranes are clinical signs of chronic mucocutaneous candidiasis, frequently associated with immunological defects. Here we describe a 39-years-old female patient, with familial CMC, that presented with an extensive infection caused by an azole-resistant Candida albicans isolate, successfully treated with posaconazole

Development of a questionnaire specifically for patients with Ileal Orthotopic Neobladder (IONB)

The ileal orthotopic neobladder (IONB) is often used in patients undergoing radical cystectomy. The IONB allows to void avoiding the disadvantages of the external urinary diversion.In IONB patients the quality of life (QoL) appears compromised by the need to urinate voluntarily. The patients need to wake up at night interrupting the sleep-wake rhythm with consequences on social and emotional life.At present the QoL in IONB patients is evaluated by generic questionnaires. These are useful when IONB patients are compared with patients with different urinary diversions but they are less effective when only IONB patients are evaluated. To address this problem a specific questionnaire-the IONB-PRO-was developed. METHODS: A) Based on a conceptual framework, narrative-based interviews were conducted on 35 IONB patients. A basic pool of 43 items was produced and organized throughout two clinical and four QoL dimensions. An additional 15 IONB patients were interviewed for face validity testing.B) Psychometric testing was conducted on 145 IONB patients. Both classic test strategy and Rasch analysis were applied. Psychometric properties of the resulting scales were comparatively tested against other QoL-validated scales. RESULTS: The IONB-PRO questionnaire includes two sections: one on the QoL and a second section on the capability of the patient to manage the IONB. For evaluation of the QoL, three versions were delivered: 1) a basic 23-item QoL version (3 domains 23-items; alpha 0.86÷ 9.69), 2) a short-form 12-item QoL scale (alpha = 0.947), and 3) a short-form 15-item Rasch QoL scale (alpha = 0.967). Correlations of the long version scales with the corresponding dimensions of the EORTC-QLQ C30 and the EORTC-BLM30 were significant. The short forms exhibited significant correlations with the global health dimension of the EORTC-QLQ and with the urinary subscales of the EORTC-BLM30. The effect size was approximately 1.00 between patients at the 1-year follow-up period and those with 3, 5, and > 5-year follow-up periods for all scales. No relevant differences were observed between the 12-item short-form and the Rasch scale. CONCLUSIONS: The IONB-PRO long and short-forms demonstrated a high level of internal consistency and reliability with an excellent discriminanting validity

Criteri dell'Informazione e Selezione dei Modelli in Misurazione Funzionale

The processes of evaluation of environmental stimuli and decision are common in everyday life and in many social and economic situations. These processes are generally described in scientific literature using multi-attribute choice models. These models assume that evaluation of a stimulus described by several attributes results from a multi-stage process (Anderson, 1981; Lynch, 1985): evaluation of the attributes, integration of the values of the attributes and explicit evaluation of the stimulus. Commonly, in this field, experimental settings require the evaluation of a set of stimuli built combining some attributes. A subject evaluator examines the attributes of each stimulus; using her “mental” model of choice (Oral & Kettani, 1989), it assigns a value to attributes and formulate an overall judgment. Finally, subject expresses his opinion in terms of order-ranking, pairwise preference comparisons, values in a rating scale, and so on. This so-called multi-attribute evaluation suffers of a fundamental difficulty to measure the values of each attribute of a stimulus starting by the overall evaluation of each subject. Basically, the problem is to derive each value decomposing the overall judgment (i.e. the response output). This difficulty in measuring is typical in most of the often complementary multi-attribute models traditions, as those of Conjoint Analysis (Luce & Tukey, 1964; Krantz & Tversky, 1971; Green & Rao, 1971) or Information Integration Theory (IIT: Anderson, 1970, 1981, 1982). According to Anderson’s IIT, cognitive system give a subjective value to each characteristic of a stimulus, and the values are put together in a overall judgment using a specific integration function. IIT describe integration modalities using different mathematical rules, and functional measurement is the methodology proposed to determine and measure the integration function. Functional measurement use factorial experiments, selecting some attributes of a stimulus and combining them in factorial plans. Usually, subject’s evaluations for each cell of experimental design are reported on a category scale, and each subject replicates each evaluation for more trials. Starting from subject’s evaluations, functional measurement aims to quantify the value of each level of factors and its importance in the global judgment, for each subject evaluator or group of subjects. Anderson’s theory suggests that the most widely used integration rules are of three fundamental and simple kinds: additive, multiplicative and weighted average. Statistical techniques as the analysis of variance can be used to detect the integration rule on the basis of the goodness of fit. The averaging rule in particular can account for interaction effects between factors, splitting evaluation in two components: scale value and weight, which can be identified and measured separately (Zalinski & Anderson, 1989). If scale value represents the location of the level of attribute on the response scale, the weight represents his importance into global judgment. Averaging model provides a useful way to manage interaction between factors, surpassing the assumption of independence on which most applications of multi-attribute choice models are based. However, the model presents some critical points about the estimation issue, and for this motivation it potential is not fully exploited up until now. In this research work, a new method for parameter estimation for averaging model is proposed. The method provides a way to select the best set of parameters to fit data, and aims to overcome some problems that have limited the use of the model. According to this new method, named R-Average (Vidotto & Vicentini, 2007; Vidotto, Massidda & Noventa, 2010), the choice of optimal model is made according to so-called “principle of parsimony”: the best model is the “simplest” one which found the best compromise between explanation of phenomenon (explained variance) and structural complexity (number of different weight parameters). Selection process use in combination two goodness-of-fit indexes: Akaike Information Criterion (AIC; Akaike, 1974) and Bayesian Information Criterion (BIC; Schwarz, 1978). Both indexes are derived starting from the logarithm of the residual variance weighted for the number of observations, and by penalizing the models with additional parameters. AIC and BIC differ in penalty function - since the BIC imposes a larger penalty for complex models than the AIC does - and are very useful for model comparison. In this research work, two version of R-Average method are presented. This two versions are one evolution of the other, and both methods are structured in some procedures to perform estimation. Basically, R-Average consists of three procedures: EAM Procedure, DAM Procedure and Information Criteria (IC) Procedure. EAM, DAM and IC differ in constraints imposed on weights during the optimization process. EAM Procedure constrains all the weight within each factor to be equal, estimating an Equal-weight Averaging Model. This model is the optimum in terms of parsimony, because it presents the smallest number of parameters (one single weight for all levels of each factor). In fact, it is defined as “parsimonious” a simple model, in which the weights are equal. Differently, DAM Procedure does not impose constraints on the weights, leaving their free to vary. Thus, this procedure may converge to a complete Differential-weight Averaging Model, which is the less parsimonious model (i.e. all the weights of each level of each factor are different). The core of R-Average method is the Information Criteria Procedure. This procedure is based on idea that, from a psychological point of view, a simple model is more plausible than a complex model. For this reason, estimation algorithm is not oriented to search parameters that explain the greater proportion of variance, but search a compromise between explained variance and model complexity. A complex model will be evaluated as better than a simpler one only if the allows a significantly higher degree of explanation of phenomenon. IC Procedure search the model, trying to keep (in the “forward” version) or to make (in the “backward” version) all the weights equal. In the forward version, the procedure starts from the EAM model and spans all the possible combination of weights, modifying it: initially one by one, then two by two, then three by three and so on. For each combination, the procedure tries to diversifies weights. From time to time, using BIC and AIC indexes, the procedure selects the best set of parameters and assume the selected model as reference for the following step (if an evidence of improvement is found). In the backward version, the procedure starts from the DAM model and spans all the possible combinations of weights, trying to equalize them. BIC and AIC are used to compare the new models with the reference model: if a new model is detected as better than the reference one, it will used as new reference for following steps. Finally, all the estimated models by the procedures are compared, and the best model based on information criteria is preferred. The original formulation of the averaging model was modified in the evolution of the basic R-Average method. This reformulation considers the weight not as simply w parameters but as w = exp(t). This exponential transformation leads to a solution for classical problem of uniqueness which affect averaging formulation (Vidotto, 2011). Furthermore, this reformulation justifies the application of cluster analysis algorithms on weight values, necessary for the clustering procedure of experimental subjects on the basis of their similarity. In fact, the distance between two t values can be evaluated in terms of simply difference. Differently, the distance between two w values can be evaluated only in terms of ratio between them. This allows to use clustering algorithms of subjects based on matrices of proximity between parameters. The performance of R-Average was tested using Monte Carlo studies and practical applications in three different research fields: in marketing, in economic decision theory and in interpersonal trust. Results of Monte Carlo studies show a good capability of the method to identify parameters of averaging model. Scale parameters are in general well estimated. Differently, weight estimation is a bit more critical. Punctual estimation of the real value of weights are not precise as the estimation of scale values, in particular as the standard deviation of the error component in observed data increases. However, estimations appears reliable, and equalities between weights are identified. The increasing of the number of experimental trials can help model selection when the errors present a greater standard deviation. In summary, R-Average appear as an useful instrument to select the best model within the family of averaging models, allowing to manage particular multi-attribute conditions in functional measurement experiments. R-Average method was applied in a first study in marketing field. In buying a product, people express a preference for particular products: understanding cognitive processes underlying the formulation of consumers’ preferences is an important issue. The study was conducted in collaboration with a local pasta manufacturer, the Sgambaro company. The aims of research were three: understand the consumer’s judgment formulation about a market product, test the R-Average method in real conditions, and provide to Sgambaro company useful information for a good marketing of its product. Two factors was manipulated: the packaging of the Sgambaro’s pasta (Box with window, Box without window and Plastic bag) and the price (0.89€, 0.99€, 1.09€). Analyses started considering evaluations of the product express by participants: for each subject, parameters of averaging model was estimated. Since the consumers population is presumably not homogeneous in preferences, the overall sample has been split in three clusters (simply named A, B and C) by an cluster analysis algorithm. For both Price and Packaging factors, different clusters showed different ratings. Cluster A express judgments that are positioned on the center of scale, suggesting as participants are not particularly attracted by this products. By contrast, Cluster B express positive judgments, and Cluster C express globally negative with the exception of the package “box with window”. For packaging, it observes that the box with window, although is not the preferred one in the three clusters, has always positive evaluations, while judgments on other packaging are inconsistent across groups. Therefore, if the target of potential consumers for the product is the general population, the box with window can be considered the most appreciated packaging. Moreover, in Cluster C ANOVA shows a significant interaction between Price and Packaging. In fact, estimated parameters of averaging model show that Cluster C is greatly affected by a high price. In this cluster the highest price had a double weight in the final ratings, therefore the positive influence on the judgment of the “box with window” packaging could be invalidated. It’s important to notice that the group which is more sensitive to the high price is also that one which gave the lowest ratings compared to the other clusters. In a second experiment, the R-Average method has been applied in a study in the field of economic decision marking under risk. The assumption that moved the study is that, when a person must evaluate an economic bet in a risky situation, person integrates cognitively the economic value of bet and the probability to win. In the past, Shanteau (1974) shown that integration between value and probability is made according a multiplicative rule. The study, as Lynch (1979), highlighted that when the situation concern two simultaneous bets, each one composed from a value and a probability, judgments for double bet is different to the sum of judgments for single bets. This observation, named subadditivity effect, violate the assumptions of Expected Utility Theory. The proposed study analyze the convenience/satisfaction associated with single and duplex bets. The study proposed to participants two kind of bets. A first group of bets involved a good (Mobile Phones), and the other one, a service (free SMS per day); to each good/service was associated the a probability to obtained him. Two experimental conditions was defined. In the first condition, subjects judge bets considering that phones come from a good company, and SMS service came from a untrustworthy provider. In the reverse condition, subjects judge bets considering that phones was made with low-quality and come from a untrustworthy company, and SMS service come from a strong and trustworthy provider. For duplex bets, the presence of averaging integration model was hypnotized, and the parameters of model was estimated using R-Average on each subject. Results show that the integration in presence of a duplex bet is fully compatible with an averaging model: the averaging and not adding appear the correct integration rule. In the last experiment, averaging model and R-Average methodology were applied to study trust beliefs in three contexts of everyday life: interpersonal, institutional and organizational. Trusting beliefs are a solid persuasion that trustee has favorable attributes to induce trusting intentions. Trusting beliefs are relevant factors in making an individual to consider another individual as trustworthy. They modulate the extent to which a trustor feels confident in believing that a trustee is trustworthy. According to McKnight, Cummings & Chervany (1998), the most cited trusting beliefs are: benevolence, competence, honesty/integrity and predictability. The basic idea under the proposed study is that beliefs might be cognitive integrated in the concept of trustworthiness with some weighting processes. The R-Average method was used to identify parameters of averaging model for each participant. As main result, analysis shown that, according to McKnight, Cummings & Chervany (1998), the four main beliefs play a fundamental role in judging trust. Moreover, agreeing with information integration theory and functional measurement, an averaging model seems to explain individual responses. The great majority of participants could be referred to the differential-weight case. While scale values show a neat linear trend with higher slopes for honesty and competence, weights show differences with higher mean values, still, for honesty and competence. These results are coherent with the idea that different attributes play a different role in the final judgment: indeed, honesty and competence seem to play the major role while predictability seems less relevant. Another interesting conclusion refers to the high weight of the low level of honesty; it seems to show how a belief related to low integrity play the most important role for a final negative judgment. Finally, the different tilt of the trend for the levels of the attributes in the three situational contexts suggests a prominent role of the honesty in the interpersonal scenarios and of the competence in the institutional scenarios. In conclusion, information integration theory and functional measurement seem to represent an interesting approach to comprehend the human judgment formulation. This research work proposes a new method to estimate parameters of averaging models. The method shows a good capability to identify parameters and opens new scenarios in information integration theory, providing a good instrument to understand more in detail the averaging integration of attributesI processi di valutazione degli stimoli ambientali e di decisione sono comuni nella vita quotidiana e in tante situazioni di carattere sociale ed economico. Questi processi sono generalmente descritti dalla letteratura scientifica utilizzando modelli di scelta multi-attributo. Tali modelli assumono che la valutazione di uno stimolo descritto da più attributi sia il risultato di un processo a più stadi (Anderson, 1981; Lynch, 1985): valutazione degli attributi, integrazione dei valori e valutazione esplicita dello stimolo. Comunemente, in questo campo, le situazioni sperimentali richiedono la valutazione di un set di stimoli costruiti combinando diversi attributi. Un soggetto valutatore esamina gli attributi di ogni stimolo; usando il solo modello “mentale” di scelta (Oral e Kettani, 1989), assegna un valore agli attributi e formula un giudizio globale. Infine, il soggetto esprime la sua opinione in termini di ordinamento, preferenze a coppie, valori su una scala numerica e così via. Questa cosiddetta valutazione multi-attributo soffre di una fondamentale difficoltà nel misurare i valori di ogni attributo di uno stimolo partendo dalle valutazioni complessive di ogni soggetto. Fondamentalmente, il problema è derivare ogni valore decomponendo il giudizio complessivo (cioè la risposta in output). Questa difficoltà di misurazione è tipica di molte delle spesso complementari tradizioni dei modelli multi-attributo, come la Conjoint Analysis (Luce e Tukey, 1964; Krantz e Tversky, 1971; Green e Rao, 1971) o la Teoria dell’Integrazione delle Informazioni (IIT: Anderson, 1970, 1981, 1982). Secondo la IIT di Anderson, il sistema cognitivo fornisce un valore soggettivo a ogni caratteristica di uno stimolo, e tali valori vengono combinati in un giudizio complessivo utilizzando una specifica funzione d’integrazione. La IIT descrive le modalità d’integrazione utilizzando differenti regole matematiche, e la misurazione funzionale è la metodologia proposta per determinare e misurare la funzione d’integrazione. La misurazione funzionale si serve di esperimenti fattoriali, selezionando alcuni attributi di uno stimolo e combinandoli in piani fattoriali. Solitamente, le valutazioni dei soggetti per ogni cella del disegno sperimentale sono riportate su una category scale, e ogni soggetto ripete la valutazione per più prove. Partendo dalle valutazioni soggettive, la misurazione funzionale mira a quantificare il valore di ogni livello dei fattori e la sua importanza nel giudizio complessivo, per ogni soggetto valutatore o gruppo di soggetti. La teoria di Anderson suggerisce che le regole d’integrazione più ampiamente utilizzate sono di tre fondamentali e semplici tipologie: additiva, moltiplicativa e di media ponderata (averaging). Tecniche statistiche come l’analisi della varianza possono essere utilizzare per individuare la regola d’integrazione sulla base della bontà dell’adattamento. La regola averaging in particolare è in grado di tenere in considerazione gli effetti d’interazione tra i fattori, scindendo la valutazione in due componenti: valore di scala e peso, che possono essere identificati e misurati separatamente (Zalisnki e Anderson, 1989). Se il valore di scala rappresenta il posizionamento del livello del fattore sulla scala di risposta, il peso rappresenta la sua importanza nel giudizio complessivo. Il modello averaging fornisce una via molto utile per gestire gli effetti d’interazione tra i fattori, superando l’assunto d’indipendenza sul quale molte applicazioni dei modelli di scelta multi-attributo sono basate. Tuttavia, il modello presenta alcuni punti critici relativi alla questione della stima, e per questo motivo il suo potenziale non è stato pienamente sfruttano fin’ora. In questo lavoro di ricerca viene proposto un nuovo metodo per la stima dei parametri del modello averaging. Il metodo consente di selezionare il miglior set di parametri per adattare i dati, e mira a superare alcuni problemi che ne hanno limitato l’uso. Secondo questo nuovo metodo, chiamato R-Average (Vidotto e Vicentini, 2007; Vidotto, Massidda e Noventa, 2010), la scelta del miglior modello è fatta in accordo al cosiddetto “principio di parsimonia”: il miglior modello è quello più “semplice”, che trova il miglior compromesso tra spiegazione del fenomeno (varianza spiegata) e complessità strutturale (numero di parametri di peso diversi). Il processo di selezione usa in combinazione due indici di bontà dell’adattamento: l’Akaike Information Criterion (AIC; Akaike, 1974) e il Bayesian Information Criterion (BIC; Schwartz, 1978). Entrambi gli indici sono ricavati partendo dal logaritmo della varianza residua pesata per il numero di osservazioni, e penalizzando i modelli con parametri aggiuntivi. AIC e BIC differiscono nella funzione di penalizzazione – dato che il BIC impone una penalità maggiore ai modelli con più parametri – e sono molto utili per la comparazione fra modelli. In questo lavoro di ricerca vengono presentate due versioni del metodo R-Average. Queste due versioni sono una l’evoluzione dell’altra, ed entrambi i metodi sono strutturati in diverse procedure per eseguire la stima. Fondamentalmente, R-Average consta di tre procedure: procedura EAM, procedura DAM e procedura Information Criteria (IC). EAM, DAM e IC differiscono nei vincoli imposti sui pesi durante il processo di ottimizzazione. La procedura EAM vincola tutti i pesi all’interno di ogni fattore a essere uguali, stimando un modello a pesi uguali. Questo modello è il migliore in termini di parsimonia, perché presenta il minor numero di parametri (uno unico per ogni fattore). Infatti, si definisce come “parsimonioso” un modello semplice, nel quale i pesi sono uguali. Diversamente, la procedura DAM non impone alcun vincolo sui pesi, lasciandoli liberi di variare. Così, questa procedura può potenzialmente convergere verso un modello averaging a pesi completamente diversi (dove cioè tutti i pesi dei livelli di ogni fattore sono diversi). Il cuore del metodo

Visuospatial working memory and early math skills in first grade children

This study aimed to investigate the relationship between different components of active visuospatial working memory and math ability in young children. In a longitudinal study, we compared the contributions of active visual and spatial working memory (WM) tasks in early math performance at two times: the beginning of the first class of primary school (T1) and the end of the first class of primary school (T2). Two tests were conducted with 43 young participants to investigate active visual WM (Imaginative Puzzles) and active spatial WM (Corsi Backward). Measurements related to pre-math ability (BIN 4-6 test) at T1 and math skills (AC-MT 6-11 test) at T2 were accomplished. The relationship between visual and spatial WM and math ability was analyzed using a regression model in which the predictors were identified through a forward selection based on the use of the BIC index (Bayesian Information Criterion). Results show that at the beginning of primary school, basic knowledge of magnitude and numbers is strongly influenced by spatial WM. T1 pre-math performance is the sole predictor of mathematical performance at T2. These results suggest different implications of domain-general and domain-specific variable on early math performance, depending on the child’s development period. This finding brings additional evidence to the debate on the relationship between visuospatial WM and math ability in young children