Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent r...Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition techniques.Facing with the challenge,a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval(IR)model and a Hybrid Intention Recognition(HIR)model.The target data acquired by the sensors are modelled as Basic Probability Assignments(BPAs)based on evidence theory to create uncertain datasets.Then,the HIR model is utilized to recognize intent for a tested sample from uncertain datasets.Finally,the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested sample.Data retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample attributes.The simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention.展开更多
This paper presents the Bayes estimation and empirical Bayes estimation of causal effects in a counterfactual model. It also gives three kinds of prior distribution of the assumptions of replaceability. The experiment...This paper presents the Bayes estimation and empirical Bayes estimation of causal effects in a counterfactual model. It also gives three kinds of prior distribution of the assumptions of replaceability. The experiment shows that empirical Bayes estimation is better than other estimations when not knowing which assumption is true.展开更多
Matching is a routinely used technique to balance covariates and thereby alleviate confounding bias in causal inference with observational data.Most of the matching literatures involve the estimating of propensity sco...Matching is a routinely used technique to balance covariates and thereby alleviate confounding bias in causal inference with observational data.Most of the matching literatures involve the estimating of propensity score with parametric model,which heavily depends on the model specification.In this paper,we employ machine learning and matching techniques to learn the average causal effect.By comparing a variety of machine learning methods in terms of propensity score under extensive scenarios,we find that the ensemble methods,especially generalized random forests,perform favorably with others.We apply all the methods to the data of tropical storms that occurred on the mainland of China since 1949.展开更多
Counterfactual model is put forward to discuss the causal inference in the directed acyclic graph and its corresponding identifiability is thus studied with the ancillary information based on conditional independence....Counterfactual model is put forward to discuss the causal inference in the directed acyclic graph and its corresponding identifiability is thus studied with the ancillary information based on conditional independence. It is shown that the assumption of ignorability can be expanded to the assumption of replaceability, under which the causal effects are identifiable.展开更多
We consider the estimation of causal treatment effect using nonparametric regression orinverse propensity weighting together with sufficient dimension reduction for searching lowdimensional covariate subsets. A specia...We consider the estimation of causal treatment effect using nonparametric regression orinverse propensity weighting together with sufficient dimension reduction for searching lowdimensional covariate subsets. A special case of this problem is the estimation of a responseeffect with data having ignorable missing response values. An issue that is not well addressedin the literature is whether the estimation of the low-dimensional covariate subsets by sufficient dimension reduction has an impact on the asymptotic variance of the resulting causaleffect estimator. With some incorrect or inaccurate statements, many researchers believe thatthe estimation of the low-dimensional covariate subsets by sufficient dimension reduction doesnot affect the asymptotic variance. We rigorously establish a result showing that this is nottrue unless the low-dimensional covariate subsets include some covariates superfluous for estimation, and including such covariates loses efficiency. Our theory is supplemented by somesimulation results.展开更多
BACKGROUND Anxiety is common in patients with inflammatory bowel disease(IBD),including those with ulcerative colitis(UC)and Crohn’s disease(CD);however,the causal relationship between IBD and anxiety remains unknown...BACKGROUND Anxiety is common in patients with inflammatory bowel disease(IBD),including those with ulcerative colitis(UC)and Crohn’s disease(CD);however,the causal relationship between IBD and anxiety remains unknown.AIM To investigate the causal relationship between IBD and anxiety by using bidirectional Mendelian randomization analysis.METHODS Single nucleotide polymorphisms retrieved from genome-wide association studies(GWAS)of the European population were identified as genetic instrument variants.GWAS statistics for individuals with UC(6968 patients and 20464 controls;adults)and CD(5956 patients and 14927 controls;adults)were obtained from the International IBD Genetics Consortium.GWAS statistics for individuals with anxiety were obtained from the Psychiatric Genomics Consortium(2565 patients and 14745 controls;adults)and FinnGen project(20992 patients and 197800 controls;adults),respectively.Inverse-variance weighted was applied to assess the causal relationship,and the results were strengthened by heterogeneity,pleiotropy and leave-one-out analyses.RESULTS Genetic susceptibility to UC was associated with an increased risk of anxiety[odds ratio:1.071(95%confidence interval:1.009-1.135),P=0.023],while genetic susceptibility to CD was not associated with anxiety.Genetic susceptibility to anxiety was not associated with UC or CD.No heterogeneity or pleiotropy was observed,and the leave-one-out analysis excluded the potential influence of a particular variant.CONCLUSION This study revealed that genetic susceptibility to UC was significantly associated with anxiety and highlighted the importance of early screening for anxiety in patients with UC.展开更多
Information flow among auditory and language processing-related regions implicated in the pathophysiology of auditory verbal hallucinations（AVHs） in schizophrenia（SZ） remains unclear. In this study, we used stocha...Information flow among auditory and language processing-related regions implicated in the pathophysiology of auditory verbal hallucinations（AVHs） in schizophrenia（SZ） remains unclear. In this study, we used stochastic dynamic causal modeling（s DCM） to quantify connections among the left dorsolateral prefrontal cortex（inner speech monitoring）, auditory cortex（auditory processing）, hippocampus（memory retrieval）, thalamus（information filtering）, and Broca＇s area（language production） in 17 first-episode drug-na？¨ve SZ patients with AVHs, 15 without AVHs, and 19 healthy controls using resting-state functional magnetic resonance imaging.Finally, we performed receiver operating characteristic（ROC） analysis and correlation analysis between image measures and symptoms. s DCM revealed an increasedsensitivity of auditory cortex to its thalamic afferents and a decrease in hippocampal sensitivity to auditory inputs in SZ patients with AVHs. The area under the ROC curve showed the diagnostic value of these two connections to distinguish SZ patients with AVHs from those without AVHs. Furthermore, we found a positive correlation between the strength of the connectivity from Broca＇s area to the auditory cortex and the severity of AVHs. These findings demonstrate, for the first time, augmented AVHspecific excitatory afferents from the thalamus to the auditory cortex in SZ patients, resulting in auditory perception without external auditory stimuli. Our results provide insights into the neural mechanisms underlying AVHs in SZ. This thalamic-auditory cortical-hippocampal dysconnectivity may also serve as a diagnostic biomarker of AVHs in SZ and a therapeutic target based on direct in vivo evidence.展开更多
What is sustainability? Does it only concern the environment or even socio-economic policies? It is only a question of ethics or a redefinition of industrial policy oriented towards the use of renewable energy, it can...What is sustainability? Does it only concern the environment or even socio-economic policies? It is only a question of ethics or a redefinition of industrial policy oriented towards the use of renewable energy, it can bring benefits both atmospheric and social employment. The need for the development of renewable sources can be in tune with the correct management of the territory in consideration of the fact that these sources involve the widespread implementation of small and medium-sized plants. A model of economic development based on renewable sources should respect the peculiarities and characteristics of the territories involved. It should also think of the territory as a “value” to be strengthened and used in a sustainable and integrated way and no longer as a passive platform on which to install plants. Solar thermal and photovoltaic, biomass, geothermal, hydrological, wind power are some of the sources the various countries must constantly invest. This publication is based on these concepts starting from an analysis of the employment data of the OECD “Organisation for Economic Co-operation and Development countries”, comparing them successively with the results of renewable energy productivity. The analysis was performed by analyzing a sample of 22 countries over a period of 20 years, after which the regression curve for the variables with the OLS method was created. This econometric method has allowed us to analyze the impact that renewable technologies have on the parameters of social welfare and in particular on unemployment.展开更多
Finding causality merely from observed data is a fundamental problem in science. The most basic form of this causal problem is to determine whether X leads to Y or Y leads to X in the case of joint observation of two ...Finding causality merely from observed data is a fundamental problem in science. The most basic form of this causal problem is to determine whether X leads to Y or Y leads to X in the case of joint observation of two variables X, Y. In statistics, path analysis is used to describe the direct dependence between a set of variables. But in fact, we usually do not know the causal order between variables. However, ignoring the direction of the causal path will prevent researchers from analyzing or using causal models. In this study, we propose a method for estimating causality based on observed data. First, observed variables are cleaned and valid variables are retained. Then, a direct linear non-Gaussian acyclic graph models(DirectLiNGAM) estimates the causal order K between variables. The third step is to estimate the adjacency matrix B of the causal relationship based on K. Next, since B is not convenient for model interpretation, we use adaptive lasso to prune the causal path and variables. Further, a causal path graph and a recursive model are established. Finally, we test and debug the recursive model, obtain a causal model with good fit, and estimate the direct, indirect and total effects between causal variables. This paper overcomes the randomness assigning causal order to variables. This study is different from the researcher’s understanding of his own model by generating some form of simulation data. The simplest and relatively unsmooth statistical learning method used in this study has obvious advantages in the field of interpretable machine learning.展开更多
An improved method for estimation of causal effects from observational data is demonstrated. Applications in medicine have been few, and the purpose of the present study is to contribute new clinical insight by means ...An improved method for estimation of causal effects from observational data is demonstrated. Applications in medicine have been few, and the purpose of the present study is to contribute new clinical insight by means of this new and more sophisticated analysis. Long term effect of medication for adult ADHD patients is not resolved. A model with causal parameters to represent effect of medication was formulated, which accounts for time-varying confounding and selection-bias from loss to follow-up. The popular marginal structural model (MSM) for causal inference, of Robins et al., adjusts for time-varying confounding, but suffers from lack of robustness for misspecification in the weights. Recent work by Imai and Ratkovic?[1][2] achieves robustness in the MSM, through improved covariate balance (CBMSM). The CBMSM (freely available software) was compared with a standard fit of a MSM and a naive regression model, to give a robust estimate of the true treatment effect in 250 previously non-medicated adults, treated for one year, in a specialized ADHD outpatient clinic in Norway. Covariate balance was greatly improved, resulting in a stronger treatment effect than without this improvement. In terms of treatment effect per week, early stages seemed to have the strongest influence. An estimated average reduction of 4 units on the symptom scale assessed at 12 weeks, for hypothetical medication in the 9 - 12 weeks period compared to no medication in this period, was found. The treatment effect persisted throughout the whole year, with an estimated average reduction of 0.7 units per week on symptoms assessed at one year, for hypothetical medication in the last 13 weeks of the year, compared to no medication in this period. The present findings support a strong and causal direct and indirect effect of pharmacological treatment of adults with ADHD on improvement in symptoms, and with a stronger treatment effect than has been reported.展开更多
基金partially supported by the National Natural Science Foundation of China(No.62173272)。
文摘Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition techniques.Facing with the challenge,a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval(IR)model and a Hybrid Intention Recognition(HIR)model.The target data acquired by the sensors are modelled as Basic Probability Assignments(BPAs)based on evidence theory to create uncertain datasets.Then,the HIR model is utilized to recognize intent for a tested sample from uncertain datasets.Finally,the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested sample.Data retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample attributes.The simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention.
文摘This paper presents the Bayes estimation and empirical Bayes estimation of causal effects in a counterfactual model. It also gives three kinds of prior distribution of the assumptions of replaceability. The experiment shows that empirical Bayes estimation is better than other estimations when not knowing which assumption is true.
基金supported by the National Key Research and Development Program of China Grant 2017YFA0604903National Natural Science Foundation of China Grant(Nos.11671338,11971064)。
文摘Matching is a routinely used technique to balance covariates and thereby alleviate confounding bias in causal inference with observational data.Most of the matching literatures involve the estimating of propensity score with parametric model,which heavily depends on the model specification.In this paper,we employ machine learning and matching techniques to learn the average causal effect.By comparing a variety of machine learning methods in terms of propensity score under extensive scenarios,we find that the ensemble methods,especially generalized random forests,perform favorably with others.We apply all the methods to the data of tropical storms that occurred on the mainland of China since 1949.
基金This work was supported by the National Natural Science Foundation of China (Grant Nos. 39930160, 19871003).
文摘Counterfactual model is put forward to discuss the causal inference in the directed acyclic graph and its corresponding identifiability is thus studied with the ancillary information based on conditional independence. It is shown that the assumption of ignorability can be expanded to the assumption of replaceability, under which the causal effects are identifiable.
基金This research was partially supported through a PatientCentered Outcomes Research Institute(PCORI)Award(ME-1409-21219)This research was also supported by the National Natural Science Foundation of China(11501208)+2 种基金Fundamental Research Funds for the Central Universities,National Social Science Foundation(13BTJ009)the Chinese 111 Project grant(B14019)the U.S.National Science Foundation(DMS-1305474 and DMS-1612873).
文摘We consider the estimation of causal treatment effect using nonparametric regression orinverse propensity weighting together with sufficient dimension reduction for searching lowdimensional covariate subsets. A special case of this problem is the estimation of a responseeffect with data having ignorable missing response values. An issue that is not well addressedin the literature is whether the estimation of the low-dimensional covariate subsets by sufficient dimension reduction has an impact on the asymptotic variance of the resulting causaleffect estimator. With some incorrect or inaccurate statements, many researchers believe thatthe estimation of the low-dimensional covariate subsets by sufficient dimension reduction doesnot affect the asymptotic variance. We rigorously establish a result showing that this is nottrue unless the low-dimensional covariate subsets include some covariates superfluous for estimation, and including such covariates loses efficiency. Our theory is supplemented by somesimulation results.
基金Supported by China Postdoctoral Science Foundation,No.2021M701614Guangdong Basic and Applied Basic Research Foundation,No.2022A1515111063,No.2022A1515111045Foundation of Guangdong Provincial People’s Hospital,No.8200010545。
文摘BACKGROUND Anxiety is common in patients with inflammatory bowel disease(IBD),including those with ulcerative colitis(UC)and Crohn’s disease(CD);however,the causal relationship between IBD and anxiety remains unknown.AIM To investigate the causal relationship between IBD and anxiety by using bidirectional Mendelian randomization analysis.METHODS Single nucleotide polymorphisms retrieved from genome-wide association studies(GWAS)of the European population were identified as genetic instrument variants.GWAS statistics for individuals with UC(6968 patients and 20464 controls;adults)and CD(5956 patients and 14927 controls;adults)were obtained from the International IBD Genetics Consortium.GWAS statistics for individuals with anxiety were obtained from the Psychiatric Genomics Consortium(2565 patients and 14745 controls;adults)and FinnGen project(20992 patients and 197800 controls;adults),respectively.Inverse-variance weighted was applied to assess the causal relationship,and the results were strengthened by heterogeneity,pleiotropy and leave-one-out analyses.RESULTS Genetic susceptibility to UC was associated with an increased risk of anxiety[odds ratio:1.071(95%confidence interval:1.009-1.135),P=0.023],while genetic susceptibility to CD was not associated with anxiety.Genetic susceptibility to anxiety was not associated with UC or CD.No heterogeneity or pleiotropy was observed,and the leave-one-out analysis excluded the potential influence of a particular variant.CONCLUSION This study revealed that genetic susceptibility to UC was significantly associated with anxiety and highlighted the importance of early screening for anxiety in patients with UC.
基金supported by the National Key Basic Research and Development Program(973)(2011CB707805)the National Natural Science Foundation of China(81571651,81301199,and 81230035)the Fund for the Dissertation Submitted to Fourth Military Medical University for the Academic Degree of Doctor,China(2014D07)
文摘Information flow among auditory and language processing-related regions implicated in the pathophysiology of auditory verbal hallucinations（AVHs） in schizophrenia（SZ） remains unclear. In this study, we used stochastic dynamic causal modeling（s DCM） to quantify connections among the left dorsolateral prefrontal cortex（inner speech monitoring）, auditory cortex（auditory processing）, hippocampus（memory retrieval）, thalamus（information filtering）, and Broca＇s area（language production） in 17 first-episode drug-na？¨ve SZ patients with AVHs, 15 without AVHs, and 19 healthy controls using resting-state functional magnetic resonance imaging.Finally, we performed receiver operating characteristic（ROC） analysis and correlation analysis between image measures and symptoms. s DCM revealed an increasedsensitivity of auditory cortex to its thalamic afferents and a decrease in hippocampal sensitivity to auditory inputs in SZ patients with AVHs. The area under the ROC curve showed the diagnostic value of these two connections to distinguish SZ patients with AVHs from those without AVHs. Furthermore, we found a positive correlation between the strength of the connectivity from Broca＇s area to the auditory cortex and the severity of AVHs. These findings demonstrate, for the first time, augmented AVHspecific excitatory afferents from the thalamus to the auditory cortex in SZ patients, resulting in auditory perception without external auditory stimuli. Our results provide insights into the neural mechanisms underlying AVHs in SZ. This thalamic-auditory cortical-hippocampal dysconnectivity may also serve as a diagnostic biomarker of AVHs in SZ and a therapeutic target based on direct in vivo evidence.
文摘What is sustainability? Does it only concern the environment or even socio-economic policies? It is only a question of ethics or a redefinition of industrial policy oriented towards the use of renewable energy, it can bring benefits both atmospheric and social employment. The need for the development of renewable sources can be in tune with the correct management of the territory in consideration of the fact that these sources involve the widespread implementation of small and medium-sized plants. A model of economic development based on renewable sources should respect the peculiarities and characteristics of the territories involved. It should also think of the territory as a “value” to be strengthened and used in a sustainable and integrated way and no longer as a passive platform on which to install plants. Solar thermal and photovoltaic, biomass, geothermal, hydrological, wind power are some of the sources the various countries must constantly invest. This publication is based on these concepts starting from an analysis of the employment data of the OECD “Organisation for Economic Co-operation and Development countries”, comparing them successively with the results of renewable energy productivity. The analysis was performed by analyzing a sample of 22 countries over a period of 20 years, after which the regression curve for the variables with the OLS method was created. This econometric method has allowed us to analyze the impact that renewable technologies have on the parameters of social welfare and in particular on unemployment.
基金Supported by the National Natural Science Foundation of China(61573266)
文摘Finding causality merely from observed data is a fundamental problem in science. The most basic form of this causal problem is to determine whether X leads to Y or Y leads to X in the case of joint observation of two variables X, Y. In statistics, path analysis is used to describe the direct dependence between a set of variables. But in fact, we usually do not know the causal order between variables. However, ignoring the direction of the causal path will prevent researchers from analyzing or using causal models. In this study, we propose a method for estimating causality based on observed data. First, observed variables are cleaned and valid variables are retained. Then, a direct linear non-Gaussian acyclic graph models(DirectLiNGAM) estimates the causal order K between variables. The third step is to estimate the adjacency matrix B of the causal relationship based on K. Next, since B is not convenient for model interpretation, we use adaptive lasso to prune the causal path and variables. Further, a causal path graph and a recursive model are established. Finally, we test and debug the recursive model, obtain a causal model with good fit, and estimate the direct, indirect and total effects between causal variables. This paper overcomes the randomness assigning causal order to variables. This study is different from the researcher’s understanding of his own model by generating some form of simulation data. The simplest and relatively unsmooth statistical learning method used in this study has obvious advantages in the field of interpretable machine learning.
文摘An improved method for estimation of causal effects from observational data is demonstrated. Applications in medicine have been few, and the purpose of the present study is to contribute new clinical insight by means of this new and more sophisticated analysis. Long term effect of medication for adult ADHD patients is not resolved. A model with causal parameters to represent effect of medication was formulated, which accounts for time-varying confounding and selection-bias from loss to follow-up. The popular marginal structural model (MSM) for causal inference, of Robins et al., adjusts for time-varying confounding, but suffers from lack of robustness for misspecification in the weights. Recent work by Imai and Ratkovic?[1][2] achieves robustness in the MSM, through improved covariate balance (CBMSM). The CBMSM (freely available software) was compared with a standard fit of a MSM and a naive regression model, to give a robust estimate of the true treatment effect in 250 previously non-medicated adults, treated for one year, in a specialized ADHD outpatient clinic in Norway. Covariate balance was greatly improved, resulting in a stronger treatment effect than without this improvement. In terms of treatment effect per week, early stages seemed to have the strongest influence. An estimated average reduction of 4 units on the symptom scale assessed at 12 weeks, for hypothetical medication in the 9 - 12 weeks period compared to no medication in this period, was found. The treatment effect persisted throughout the whole year, with an estimated average reduction of 0.7 units per week on symptoms assessed at one year, for hypothetical medication in the last 13 weeks of the year, compared to no medication in this period. The present findings support a strong and causal direct and indirect effect of pharmacological treatment of adults with ADHD on improvement in symptoms, and with a stronger treatment effect than has been reported.
