Dynamic casual modeling of functional magnetic resonance imaging（fMRI） signals is employed to explore critical emotional neurocircuitry under sad stimuli. The intrinsic model of emotional loops is built on the basis...Dynamic casual modeling of functional magnetic resonance imaging（fMRI） signals is employed to explore critical emotional neurocircuitry under sad stimuli. The intrinsic model of emotional loops is built on the basis of Papez＇s circuit and related prior knowledge, and then three modulatory connection models are established. In these models, stimuli are placed at different points, which represents they affect the neural activities between brain regions, and these activities are modulated in different ways. Then, the optimal model is selected by Bayesian model comparison. From group analysis, patients＇ intrinsic and modulatory connections from the anterior cingulate cortex （ACC） to the right inferior frontal gyrus （rlFG） are significantly higher than those of the control group. Then the functional connection parameters of the model are selected as classifier features. The classification accuracy rate from the support vector machine（SVM） classifier is 80.73%, which, to some extent, validates the effectiveness of the regional connectivity parameters for depression recognition and provides a new approach for the clinical diagnosis of depression.展开更多
AIM： To investigate and test a causal model derivedfrom previous meta-analytic data of health provider be-haviors and patient satisfaction.METHODS： A literature search was conducted forrelevant manuscripts that met ...AIM： To investigate and test a causal model derivedfrom previous meta-analytic data of health provider be-haviors and patient satisfaction.METHODS： A literature search was conducted forrelevant manuscripts that met the following criteria：Reported an analysis of provider-patient interaction inthe context of an oncology interview; the study hadto measure at least two of the variables of interest tothe model （provider activity, provider patient-centeredcommunication, provider facilitative communication,patient activity, patient involvement, and patient satis-faction or reduced anxiety）; and the information had tobe reported in a manner that permitted the calculationof a zero-order correlation between at least two of thevariables under consideration. Data were transformedinto correlation coefficients and compiled to producethe correlation matrix used for data analysis. The test of the causal model is a comparison of the expected correlation matrix generated using an Ordinary Least Squares method of estimation. The expected matrix iscompared to the actual matrix of zero order correlation coeffcients. A model is considered a possible ft if the level of deviation is less than expected due to random sampling error as measured by a chi-square statistic. The signifcance of the path coeffcients was tested us-ing a z test. Lastly, the Sobel test provides a test of the level of mediation provided by a variable and provides an estimate of the level of mediation for each connec-tion. Such a test is warranted in models with multiple paths.RESULTS： A test of the original model indicated a lack of ft with the summary data. The largest discrepancy in the model was between the patient satisfaction and the provider patient-centered utterances. The observed correlation was far larger than expected given a medi-ated relationship. The test of a modifed model was un-dertaken to determine possible ft. The corrected model provides a fit to within tolerance as evaluated by the test statistic, χ2 （8, average n = 342） = 10.22. Each of the path coefficients for the model reveals that each one can be considered signifcant, P 〈 0.05. The Sobel test examining the impact of the mediating variables demonstrated that patient involvement is a signifcantmediator in the model, Sobel statistic = 3.56, P 〈 0.05. Patient active was also demonstrated to be a signifcant mediator in the model, Sobel statistic = 4.21, P 〈 0.05. The statistics indicate that patient behavior mediates the relationship between provider behavior and patient satisfaction with the interaction.CONCLUSION： The results demonstrate empirical support for the importance of patient-centered care and satisfy the need for empirical casual support of provider-patient behaviors on health outcomes.展开更多
Recently, several approaches have been proposed to discover the causality of the time-independent or fixed causal model. However, in many realistic applications, especially in economics and neuroscience, causality amo...Recently, several approaches have been proposed to discover the causality of the time-independent or fixed causal model. However, in many realistic applications, especially in economics and neuroscience, causality among variables might be time-varying. A time-varying linear causal model with non-Gaussian noise is considered and the estimation of the causal model from observational data is focused. Firstly, an independent component analysis(ICA) based two stage method is proposed to estimate the time-varying causal coefficients. It shows that, under appropriate assumptions, the time varying coefficients in the proposed model can be estimated by the proposed approach, and results of experiment on artificial data show the effectiveness of the proposed approach. And then, the granger causality test is used to ascertain the causal direction among the variables. Finally, the new approach is applied to the real stock data to identify the causality among three stock indices and the result is consistent with common sense.展开更多
The adverse effects of maternal smoking during pregnancy on both the offspring and women are well known. The main objective of this research article is to provide health professional causal modelling approach to make ...The adverse effects of maternal smoking during pregnancy on both the offspring and women are well known. The main objective of this research article is to provide health professional causal modelling approach to make a more comprehensive assessment of major determinants of smoking behaviour during and after pregnancy and consequently the outcomes of pregnant women smoking which are adversely affecting both the offspring and pregnant women. The causal model based on theory and evidence was modified and applied to material smoking cessation intervention to control the adverse effects of smoking on offspring obesity and neurodevelopment. In this approach a generic model links behavioural determinants, causally through behaviour, to physiological and biochemical variables, and health outcomes. It is tailored to context, target population, behaviours and health outcomes. The model provides a rational guide to appropriate measures, intervention points and intervention techniques, and can be tested quantitatively. The causal modelling approach showed promising results which can be used to help maternal smoking women to understand the risk of smoking and help them to quit smoking. The regression analysis of maternal smoking women BMI (n = 1000) on offspring BMI was statistically significant, p 0.05). This supported the hypothesis that maternal smoking women BMI during pregnancy is an important determinant of offspring obesity and consequently the risk factors of cardiovascular development. The causal modelling approach is unique as it provides an incentive to health professional to use these models to target any important and modifiable determinants of the maternal smoking behaviour and decrease the risk of adverse pregnancy outcomes for the offspring and the mother.展开更多
Recently there have been two causal modelling approaches to indicative conditionals,i.e.extrapolationist(Deng&Lee,2021)and filterist(Liang&Wang,2022),although they all take an interventionist position on subju...Recently there have been two causal modelling approaches to indicative conditionals,i.e.extrapolationist(Deng&Lee,2021)and filterist(Liang&Wang,2022),although they all take an interventionist position on subjunctive conditionals.Motivated by the so-called OK pairs,they try to provide a convincing explanation of the intuition underlying the OK pairs.As far as we know,what they have done is to provide not only an explanation of the OK pairs,but also a way of distinguishing between indicative and subjunctive conditionals.Although we agree with their success in explaining the OK pairs within a causal modelling framework,we argue that their ways of distinguishing between indicative and subjunctive conditionals fail.Instead,we argue that their approaches can be used to distinguish between two readings of conditionals,the epistemic reading and the ontic reading.which can be applied to both indicative and subjunctive conditionals.We conclude by arguing that these two readings are related to two approaches to asking and answering causal questions:the“auses-of-effects"approach and the"effects-of-causes"approach.展开更多
Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations.However, difficulties associated with computational overload, ubiquitous uncertainties and i...Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations.However, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engineering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied "chaining" inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.展开更多
Currently, Granger-Geweke causality models have been widely applied to investigate the dynamic direction relationships among brain regions. In a previous study, we have found that the right hand finger-tapping task ca...Currently, Granger-Geweke causality models have been widely applied to investigate the dynamic direction relationships among brain regions. In a previous study, we have found that the right hand finger-tapping task can produce relatively reliable brain response. As an extension of our previous study, we developed an algorithm based on the classical Granger- Geweke causality model to further investigate the effective connectivity of three brain regions (left primary motor cortex (M1), supplementary motor area (SMA) and right cerebellum) that showed the most robust brain activations. Our computational results not only confirm the strong linear feedback among SMA, M1 and right cerebellum, but also demonstrate that M1 is the hub of these three regions indicated by the anatomy research. Moreover, the model predicts the high intermediate node density existing in the area between SMA and M1, which will stimulate the imaging experimentalists to carry out new experiments to validate this postulation.展开更多
Aiming at the research that using more new knowledge to develope knowledge system with dynamic accordance, and under the background of using Fuzzy language field and Fuzzy language values structure as description fram...Aiming at the research that using more new knowledge to develope knowledge system with dynamic accordance, and under the background of using Fuzzy language field and Fuzzy language values structure as description framework, the generalized cell Automation that can synthetically process fuzzy indeterminacy and random indeterminacy and generalized inductive logic causal model is brought forward. On this basis, a kind of the new method that can discover causal association rules is provded. According to the causal information of standard sample space and commonly sample space, through constructing its state （abnormality） relation matrix, causal association rules can be gained by using inductive reasoning mechanism. The estimate of this algorithm complexity is given,and its validiw is proved through case.展开更多
Regression is a widely used econometric tool in research. In observational studies, based on a number of assumptions, regression-based statistical control methods attempt to analyze the causation between treatment and...Regression is a widely used econometric tool in research. In observational studies, based on a number of assumptions, regression-based statistical control methods attempt to analyze the causation between treatment and outcome by adding control variables. However, this approach may not produce reliable estimates of causal effects. In addition to the shortcomings of the method, this lack of confidence is mainly related to ambiguous formulations in econometrics, such as the definition of selection bias, selection of core control variables, and method of testing for robustness. Within the framework of the causal models, we clarify the assumption of causal inference using regression-based statistical controls, as described in econometrics, and discuss how to select core control variables to satisfy this assumption and conduct robustness tests for regression estimates.展开更多
基金The National Natural Science Foundation of China(No.30900356,81071135)
文摘Dynamic casual modeling of functional magnetic resonance imaging（fMRI） signals is employed to explore critical emotional neurocircuitry under sad stimuli. The intrinsic model of emotional loops is built on the basis of Papez＇s circuit and related prior knowledge, and then three modulatory connection models are established. In these models, stimuli are placed at different points, which represents they affect the neural activities between brain regions, and these activities are modulated in different ways. Then, the optimal model is selected by Bayesian model comparison. From group analysis, patients＇ intrinsic and modulatory connections from the anterior cingulate cortex （ACC） to the right inferior frontal gyrus （rlFG） are significantly higher than those of the control group. Then the functional connection parameters of the model are selected as classifier features. The classification accuracy rate from the support vector machine（SVM） classifier is 80.73%, which, to some extent, validates the effectiveness of the regional connectivity parameters for depression recognition and provides a new approach for the clinical diagnosis of depression.
文摘AIM： To investigate and test a causal model derivedfrom previous meta-analytic data of health provider be-haviors and patient satisfaction.METHODS： A literature search was conducted forrelevant manuscripts that met the following criteria：Reported an analysis of provider-patient interaction inthe context of an oncology interview; the study hadto measure at least two of the variables of interest tothe model （provider activity, provider patient-centeredcommunication, provider facilitative communication,patient activity, patient involvement, and patient satis-faction or reduced anxiety）; and the information had tobe reported in a manner that permitted the calculationof a zero-order correlation between at least two of thevariables under consideration. Data were transformedinto correlation coefficients and compiled to producethe correlation matrix used for data analysis. The test of the causal model is a comparison of the expected correlation matrix generated using an Ordinary Least Squares method of estimation. The expected matrix iscompared to the actual matrix of zero order correlation coeffcients. A model is considered a possible ft if the level of deviation is less than expected due to random sampling error as measured by a chi-square statistic. The signifcance of the path coeffcients was tested us-ing a z test. Lastly, the Sobel test provides a test of the level of mediation provided by a variable and provides an estimate of the level of mediation for each connec-tion. Such a test is warranted in models with multiple paths.RESULTS： A test of the original model indicated a lack of ft with the summary data. The largest discrepancy in the model was between the patient satisfaction and the provider patient-centered utterances. The observed correlation was far larger than expected given a medi-ated relationship. The test of a modifed model was un-dertaken to determine possible ft. The corrected model provides a fit to within tolerance as evaluated by the test statistic, χ2 （8, average n = 342） = 10.22. Each of the path coefficients for the model reveals that each one can be considered signifcant, P 〈 0.05. The Sobel test examining the impact of the mediating variables demonstrated that patient involvement is a signifcantmediator in the model, Sobel statistic = 3.56, P 〈 0.05. Patient active was also demonstrated to be a signifcant mediator in the model, Sobel statistic = 4.21, P 〈 0.05. The statistics indicate that patient behavior mediates the relationship between provider behavior and patient satisfaction with the interaction.CONCLUSION： The results demonstrate empirical support for the importance of patient-centered care and satisfy the need for empirical casual support of provider-patient behaviors on health outcomes.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61573014)
文摘Recently, several approaches have been proposed to discover the causality of the time-independent or fixed causal model. However, in many realistic applications, especially in economics and neuroscience, causality among variables might be time-varying. A time-varying linear causal model with non-Gaussian noise is considered and the estimation of the causal model from observational data is focused. Firstly, an independent component analysis(ICA) based two stage method is proposed to estimate the time-varying causal coefficients. It shows that, under appropriate assumptions, the time varying coefficients in the proposed model can be estimated by the proposed approach, and results of experiment on artificial data show the effectiveness of the proposed approach. And then, the granger causality test is used to ascertain the causal direction among the variables. Finally, the new approach is applied to the real stock data to identify the causality among three stock indices and the result is consistent with common sense.
文摘The adverse effects of maternal smoking during pregnancy on both the offspring and women are well known. The main objective of this research article is to provide health professional causal modelling approach to make a more comprehensive assessment of major determinants of smoking behaviour during and after pregnancy and consequently the outcomes of pregnant women smoking which are adversely affecting both the offspring and pregnant women. The causal model based on theory and evidence was modified and applied to material smoking cessation intervention to control the adverse effects of smoking on offspring obesity and neurodevelopment. In this approach a generic model links behavioural determinants, causally through behaviour, to physiological and biochemical variables, and health outcomes. It is tailored to context, target population, behaviours and health outcomes. The model provides a rational guide to appropriate measures, intervention points and intervention techniques, and can be tested quantitatively. The causal modelling approach showed promising results which can be used to help maternal smoking women to understand the risk of smoking and help them to quit smoking. The regression analysis of maternal smoking women BMI (n = 1000) on offspring BMI was statistically significant, p 0.05). This supported the hypothesis that maternal smoking women BMI during pregnancy is an important determinant of offspring obesity and consequently the risk factors of cardiovascular development. The causal modelling approach is unique as it provides an incentive to health professional to use these models to target any important and modifiable determinants of the maternal smoking behaviour and decrease the risk of adverse pregnancy outcomes for the offspring and the mother.
基金supported by China’s MOE project of Key Research Institute of Humanities and Social Sciences at Universities(22JJD720021)the project of Shandong University(11090087395308).
文摘Recently there have been two causal modelling approaches to indicative conditionals,i.e.extrapolationist(Deng&Lee,2021)and filterist(Liang&Wang,2022),although they all take an interventionist position on subjunctive conditionals.Motivated by the so-called OK pairs,they try to provide a convincing explanation of the intuition underlying the OK pairs.As far as we know,what they have done is to provide not only an explanation of the OK pairs,but also a way of distinguishing between indicative and subjunctive conditionals.Although we agree with their success in explaining the OK pairs within a causal modelling framework,we argue that their ways of distinguishing between indicative and subjunctive conditionals fail.Instead,we argue that their approaches can be used to distinguish between two readings of conditionals,the epistemic reading and the ontic reading.which can be applied to both indicative and subjunctive conditionals.We conclude by arguing that these two readings are related to two approaches to asking and answering causal questions:the“auses-of-effects"approach and the"effects-of-causes"approach.
基金supported by the National Natural Science Foundation of China(Nos.61050005 and 61273330)Research Foundation for the Doctoral Program of China Ministry of Education(No.20120002110037)+1 种基金the 2014 Teaching Reform Project of Shandong Normal UniversityDevelopment Project of China Guangdong Nuclear Power Group(No.CNPRI-ST10P005)
文摘Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations.However, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engineering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied "chaining" inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.
文摘Currently, Granger-Geweke causality models have been widely applied to investigate the dynamic direction relationships among brain regions. In a previous study, we have found that the right hand finger-tapping task can produce relatively reliable brain response. As an extension of our previous study, we developed an algorithm based on the classical Granger- Geweke causality model to further investigate the effective connectivity of three brain regions (left primary motor cortex (M1), supplementary motor area (SMA) and right cerebellum) that showed the most robust brain activations. Our computational results not only confirm the strong linear feedback among SMA, M1 and right cerebellum, but also demonstrate that M1 is the hub of these three regions indicated by the anatomy research. Moreover, the model predicts the high intermediate node density existing in the area between SMA and M1, which will stimulate the imaging experimentalists to carry out new experiments to validate this postulation.
文摘Aiming at the research that using more new knowledge to develope knowledge system with dynamic accordance, and under the background of using Fuzzy language field and Fuzzy language values structure as description framework, the generalized cell Automation that can synthetically process fuzzy indeterminacy and random indeterminacy and generalized inductive logic causal model is brought forward. On this basis, a kind of the new method that can discover causal association rules is provded. According to the causal information of standard sample space and commonly sample space, through constructing its state （abnormality） relation matrix, causal association rules can be gained by using inductive reasoning mechanism. The estimate of this algorithm complexity is given,and its validiw is proved through case.
基金This research was funded by the National Natural Science Foundation of China(Grant No.72074060).
文摘Regression is a widely used econometric tool in research. In observational studies, based on a number of assumptions, regression-based statistical control methods attempt to analyze the causation between treatment and outcome by adding control variables. However, this approach may not produce reliable estimates of causal effects. In addition to the shortcomings of the method, this lack of confidence is mainly related to ambiguous formulations in econometrics, such as the definition of selection bias, selection of core control variables, and method of testing for robustness. Within the framework of the causal models, we clarify the assumption of causal inference using regression-based statistical controls, as described in econometrics, and discuss how to select core control variables to satisfy this assumption and conduct robustness tests for regression estimates.
