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
目的利用真实世界医疗数据,针对心脏病患者研究硝苯地平和美托洛尔药物在主要心血管事件、全因死亡和药源性肝损伤中的作用。方法基于齐鲁全生命周期电子研究型数据库(Cheeloo Lifespan Electronic Health Research Data-library,Cheelo...目的利用真实世界医疗数据,针对心脏病患者研究硝苯地平和美托洛尔药物在主要心血管事件、全因死亡和药源性肝损伤中的作用。方法基于齐鲁全生命周期电子研究型数据库(Cheeloo Lifespan Electronic Health Research Data-library,Cheeloo LEAD),选取2012年1月1日至2022年12月31日期间首次诊断为心脏病的患者作为研究对象,采用倾向性评分匹配的新使用者队列设计,开展真实世界研究。通过大规模L1正则化倾向性评分方法进行协变量匹配,实现拟随机化处理,采用Cox比例风险回归模型评估硝苯地平(暴露组)和美托洛尔(对照组)分别对主要心血管事件、全因死亡和药源性肝损伤的平均因果效应。结果男性心脏病患者更易出现主要心血管事件、全因死亡和药源性肝损伤。L1正则化倾向性评分匹配后,协变量标准化差异全部在0.2以下且绝大部分在0.1以下,暴露组和对照组的生存曲线无明显交叉。药源性肝损伤与全因死亡的发生率在组间比较中,log-rank检验结果均具有统计学意义(P<0.05)。此外,使用硝苯地平的患者在随访期内的累积生存率高于使用美托洛尔的患者。Cox比例风险回归模型显示,硝苯地平较美托洛尔对药源性肝损伤风险更低(HR=0.39,95%CI:0.16~0.93,P=0.025)且具有更低的全因死亡风险(HR=0.53,95%CI:0.28~0.98,P=0.034),未发现硝苯地平和美托洛尔预防主要心血管事件的效果差异(HR=1.30,95%CI:0.97~1.90,P=0.061)。结论相较于美托洛尔,硝苯地平可降低心脏病患者的全国死亡风险,且其药源性肝损伤的风险更低,本研究结果可以为心脏病患者安全用药提供补充证据,尤其可为评估药源性肝损伤风险提供参考。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
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.
文摘目的利用真实世界医疗数据,针对心脏病患者研究硝苯地平和美托洛尔药物在主要心血管事件、全因死亡和药源性肝损伤中的作用。方法基于齐鲁全生命周期电子研究型数据库(Cheeloo Lifespan Electronic Health Research Data-library,Cheeloo LEAD),选取2012年1月1日至2022年12月31日期间首次诊断为心脏病的患者作为研究对象,采用倾向性评分匹配的新使用者队列设计,开展真实世界研究。通过大规模L1正则化倾向性评分方法进行协变量匹配,实现拟随机化处理,采用Cox比例风险回归模型评估硝苯地平(暴露组)和美托洛尔(对照组)分别对主要心血管事件、全因死亡和药源性肝损伤的平均因果效应。结果男性心脏病患者更易出现主要心血管事件、全因死亡和药源性肝损伤。L1正则化倾向性评分匹配后,协变量标准化差异全部在0.2以下且绝大部分在0.1以下,暴露组和对照组的生存曲线无明显交叉。药源性肝损伤与全因死亡的发生率在组间比较中,log-rank检验结果均具有统计学意义(P<0.05)。此外,使用硝苯地平的患者在随访期内的累积生存率高于使用美托洛尔的患者。Cox比例风险回归模型显示,硝苯地平较美托洛尔对药源性肝损伤风险更低(HR=0.39,95%CI:0.16~0.93,P=0.025)且具有更低的全因死亡风险(HR=0.53,95%CI:0.28~0.98,P=0.034),未发现硝苯地平和美托洛尔预防主要心血管事件的效果差异(HR=1.30,95%CI:0.97~1.90,P=0.061)。结论相较于美托洛尔,硝苯地平可降低心脏病患者的全国死亡风险,且其药源性肝损伤的风险更低,本研究结果可以为心脏病患者安全用药提供补充证据,尤其可为评估药源性肝损伤风险提供参考。
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.
