Currently,various rapidly developing information technologies are gradually transforming traditional social systems into Complex Social Systems(CSS).On the one hand,individuals'ability to make decisions and access...Currently,various rapidly developing information technologies are gradually transforming traditional social systems into Complex Social Systems(CSS).On the one hand,individuals'ability to make decisions and access information is increasing,making their behaviors more unpredictable.On the other hand,technology is facilitating an increase in the intensity and scope of individual interactions,with cascade effects making the outcomes of interactions difficult to estimate.To improve the performance of CSS,it is essential to examine the causal laws that determine what kind of performance the system exhibits.However,researches on the causal laws of CSS remain scarce,leading to the lack of foundations for analyzing such systems.Inspired by computational experiments and causal analysis,this paper proposes a Hierarchical Causal Model(HCM)with three layers,each of which presents,extracts,and applies the causality.We apply the proposed model to enhance the system performance in a typical CSS,a software-enabled small-scale plant.Experimental results show that 98.38%of the working days have better system performance than the actual performance after applying our proposed model,and the mean of the median improvement reaches 41.38%.These results validate the proposed model,demonstrating that this work provides a feasible method for the analysis of CSS.展开更多
With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or p...With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.展开更多
Rational use of blast furnace gas(BFG) in steel industry can raise economic profit, save fossil energy resources and alleviate the environment pollution. In this paper, a causality diagram is established to describe t...Rational use of blast furnace gas(BFG) in steel industry can raise economic profit, save fossil energy resources and alleviate the environment pollution. In this paper, a causality diagram is established to describe the causal relationships among the decision objective and the variables of the scheduling process for the industrial system, based on which the total scheduling amount of the BFG system can be computed by using a causal fuzzy C-means(CFCM) clustering algorithm. In this algorithm,not only the distances among the historical samples but also the effects of different solutions on the gas tank level are considered.The scheduling solution can be determined based on the proposed causal probability of the causality diagram calculated by the total amount and the conditions of the adjustable units. The causal probability quantifies the impact of different allocation schemes of the total scheduling amount on the BFG system. An evaluation method is then proposed to evaluate the effectiveness of the scheduling solutions. The experiments by using the practical data coming from a steel plant in China indicate that the proposed approach can effectively improve the scheduling accuracy and reduce the gas diffusion.展开更多
Causality Diagram (CD) is a new graphical knowledge representation based on probability theory. The application of this methodology in the safety analysis of the gas explosion in collieries was discussed in this paper...Causality Diagram (CD) is a new graphical knowledge representation based on probability theory. The application of this methodology in the safety analysis of the gas explosion in collieries was discussed in this paper, and the Minimal Cut Set, the Minimal Path Set and the Importance were introduced to develop the methodology. These concepts are employed to analyze the influence each event has on the top event ? the gas explosion, so as to find out about the defects of the system and accordingly help to work out the emphasis of the precautionary work and some preventive measures as well. The results of the safety analysis are in accordance with the practical requirements; therefore the preventive measures are certain to work effectively. In brief, according to the research CD is so effective in the safety analysis and the safety assessment that it can be a qualitative and quantitative method to predict the accident as well as offer some effective measures for the investigation, the prevention and the control of the accident.展开更多
The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from...The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007-2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance.展开更多
The Causal relation and whole-part relation are the two fundamen-tal relations in economics.In this paper,on the basis of economic prob-lems analysis in practioce,some causal relation structures of economic sys-tems a...The Causal relation and whole-part relation are the two fundamen-tal relations in economics.In this paper,on the basis of economic prob-lems analysis in practioce,some causal relation structures of economic sys-tems and fundamental rules for operations research are,at first,prop-osed.And then,a quotienting(simplifying)analysis approach of eco-nomic causal relations is presented and discussed in detail.At last,animprovement framework of system dynamics is proposed based on theviewpoint of generalized causal relations analysis.展开更多
The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitatio...The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitations:1)unrealistic assumptions of complete evidence independence,and 2)a lack of mechanisms to differentiate causal relationships from spurious correlations.Existing similarity-based approaches often misinterpret interdependent evidence,leading to unreliable decision outcomes.To address these gaps,this study proposes a causality-enhanced ER rule(CER-e)framework with three key methodological innovations:1)a multidimensional causal representation of evidence to capture dependency structures;2)probabilistic quantification of causal strength using transfer entropy,a model-free information-theoretic measure;3)systematic integration of causal parameters into the ER inference process while maintaining evidential objectivity.The PC algorithm is employed during causal discovery to eliminate spurious correlations,ensuring robust causal inference.Case studies in two types of domains—telecommunications network security assessment and structural risk evaluation—validate CER-e’s effectiveness in real-world scenarios.Under simulated incomplete information conditions,the framework demonstrates superior algorithmic robustness compared to traditional ER.Comparative analyses show that CER-e significantly improves both the interpretability of causal relationships and the reliability of assessment results,establishing a novel paradigm for integrating causal inference with evidential reasoning in complex system evaluation.展开更多
Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(M...Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(ML)nature,causal models hope to tie the cause(s)to the effect(s)pertaining to a phenomenon(i.e.,data generating process)through causal principles.This paper presents one of the first works at creating causal models in the area of structural and construction engineering.To this end,this paper starts with a brief review of the principles of causality and then adopts four causal discovery algorithms,namely,PC(Peter-Clark),FCI(fast causal inference),GES(greedy equivalence search),and GRa SP(greedy relaxation of the sparsest permutation),have been used to examine four phenomena,including predicting the load-bearing capacity of axially loaded members,fire resistance of structural members,shear strength of beams,and resistance of walls against impulsive(blast)loading.Findings from this study reveal the possibility and merit of discovering complete and partial causal models.Finally,this study also proposes two simple metrics that can help assess the performance of causal discovery algorithms.展开更多
Emerging evidence highlights the role of thyroid hormones in cancer,although findings are controversial.Research on thyroid-related traits in lung carcinogenesis is limited.Using UK Biobank data,we performed bidirecti...Emerging evidence highlights the role of thyroid hormones in cancer,although findings are controversial.Research on thyroid-related traits in lung carcinogenesis is limited.Using UK Biobank data,we performed bidirectional Mendelian randomization(MR)to assess causal associations between lung cancer risk and thyroid dysfunction(hypothyroidism and hyperthyroidism)or functional traits(free thyroxine[FT4]and normal-range thyroid-stimulating hormone[TSH]).Furthermore,in the smoking-behavior-stratified MR analysis,we evaluated the mediating effect of thyroid-related phenotypes on the association between smoking behaviors and lung cancer.We demonstrated significant associations between lung cancer risk and hypothyroidism(hazard ratio[HR]=1.14,95%confidence interval[CI]=1.03–1.26,P=0.009)and hyperthyroidism(HR=1.55,95%CI=1.29–1.87,P=1.90×10^(-6))in the UKB.Moreover,the MR analysis indicated a causal effect of thyroid dysfunction on lung cancer risk(ORinverse variance weighted[IVW]=1.09,95%CI=1.05–1.13,P=3.12×10^(-6)for hypothyroidism;ORIVW=1.08,95%CI=1.04–1.12,P=8.14×10^(-5)for hyperthyroidism).We found that FT4 levels were protective against lung cancer risk(ORIVW=0.93,95%CI=0.87–0.99,P=0.030).Additionally,the stratified MR analysis demonstrated distinct causal effects of thyroid dysfunction on lung cancer risk among smokers.Hyperthyroidism mediated the effect of smoking behaviors,especially the age of smoking initiation(17.66%mediated),on lung cancer risk.Thus,thyroid dysfunction phenotypes play causal roles in lung cancer development exclusively among smokers and act as mediators in the causal pathway from smoking to lung cancer.展开更多
Dear Editor,Observational studies in epidemiology have identified a correlation between hypothyroidism and cholelithiasis[1–2].However,the causal relationship between the two diseases remains unclear.To investigate t...Dear Editor,Observational studies in epidemiology have identified a correlation between hypothyroidism and cholelithiasis[1–2].However,the causal relationship between the two diseases remains unclear.To investigate the potential causal relationship,we employed a two-sample bidirectional Mendelian randomization(MR)analysis.展开更多
The isotope effect on zonal flows(ZFs)and turbulence remains a key issue that is not completely solved in fusion plasmas.This paper presents the first experimental results of the ab initio prediction of causal relatio...The isotope effect on zonal flows(ZFs)and turbulence remains a key issue that is not completely solved in fusion plasmas.This paper presents the first experimental results of the ab initio prediction of causal relation between geodesic acoustic mode(GAM)and ambient turbulence at different isotope masses in the edge of HL-2A tokamak,where transfer entropy method based on information-theoretical approach is utilized as a quantified indicator of causality.Analysis shows that GAM is more pronounced in deuterium plasmas than in hydrogen,leading to a lower heat transport as well as more peaked profiles in the former situation.The causal impact of GAM on conductive heat flux component is stronger than on the convective component,which is resulted from a larger causal influence of zonal flow on temperature fluctuation.While a stronger GAM in deuterium plasmas has larger influence on all flux components,the relative change in temperature fluctuation and coefficient is more obvious when the ion mass varies.These findings not only offer an in-depth understanding of the real causality between zonal flow and turbulence in the present isotope experiments,but also provide useful ways for the physical understandings of transport and zonal flow dynamics in future deuterium-tritium fusion plasmas.展开更多
Offshore drilling costs are high,and the downhole environment is even more complex.Improving the rate of penetration(ROP)can effectively shorten offshore drilling cycles and improve economic benefits.It is difficult f...Offshore drilling costs are high,and the downhole environment is even more complex.Improving the rate of penetration(ROP)can effectively shorten offshore drilling cycles and improve economic benefits.It is difficult for the current ROP models to guarantee the prediction accuracy and the robustness of the models at the same time.To address the current issues,a new ROP prediction model was developed in this study,which considers ROP as a time series signal(ROP signal).The model is based on the time convolutional network(TCN)framework and integrates ensemble empirical modal decomposition(EEMD)and Bayesian network causal inference(BN),the model is named EEMD-BN-TCN.Within the proposed model,the EEMD decomposes the original ROP signal into multiple sets of sub-signals.The BN determines the causal relationship between the sub-signals and the key physical parameters(weight on bit and revolutions per minute)and carries out preliminary reconstruction of the sub-signals based on the causal relationship.The TCN predicts signals reconstructed by BN.When applying this model to an actual production well,the average absolute percentage error of the EEMD-BN-TCN prediction decreased from 18.4%with TCN to 9.2%.In addition,compared with other models,the EEMD-BN-TCN can improve the decomposition signal of ROP by regulating weight on bit and revolutions per minute,ultimately enhancing ROP.展开更多
Understanding the characteristics and driving factors behind changes in vegetation ecosystem resilience is crucial for mitigating both current and future impacts of climate change. Despite recent advances in resilienc...Understanding the characteristics and driving factors behind changes in vegetation ecosystem resilience is crucial for mitigating both current and future impacts of climate change. Despite recent advances in resilience research, significant knowledge gaps remain regarding the drivers of resilience changes. In this study, we investigated the dynamics of ecosystem resilience across China and identified potential driving factors using the kernel normalized difference vegetation index(kNDVI) from 2000 to 2020. Our results indicate that vegetation resilience in China has exhibited an increasing trend over the past two decades, with a notable breakpoint occurring around 2012. We found that precipitation was the dominant driver of changes in ecosystem resilience, accounting for 35.82% of the variation across China, followed by monthly average maximum temperature(Tmax) and vapor pressure deficit(VPD), which explained 28.95% and 28.31% of the variation, respectively. Furthermore, we revealed that daytime and nighttime warming has asymmetric impacts on vegetation resilience, with temperature factors such as Tmin and Tmax becoming more influential, while the importance of precipitation slightly decreases after the resilience change point. Overall, our study highlights the key roles of water availability and temperature in shaping vegetation resilience and underscores the asymmetric effects of daytime and nighttime warming on ecosystem resilience.展开更多
The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,direc...The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,directly predicting missing components via global characteristics extracted from incomplete inputs.However,this makes detail re-covery challenging,as global characteristics fail to provide complete missing component specifics.A new point cloud completion method named Point-PC is proposed.A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion.Concretely,a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format.The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain,enabling retrieval of analogous shapes from incomplete inputs.In addition,the authors employ backdoor adjustment to eliminate confounders,which are shape prior components sharing identical semantic structures with incomplete inputs.Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches.The code for Point-PC can be accessed by https://github.com/bizbard/Point-PC.git.展开更多
The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to...The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to falling into local optima.To address these issues,an improved GA with domain knowledge(IGADK)is proposed.Firstly,domain knowledge is incorporated into the learning process of causality to construct a new fitness function.Secondly,a dynamical mutation operator is introduced in the algorithm to accelerate the convergence rate.Finally,an experiment is conducted on simulation data,which compares the classical GA with IGADK with domain knowledge of varying accuracy.The IGADK can greatly reduce the number of iterations,populations,and samples required for learning,which illustrates the efficiency and effectiveness of the proposed algorithm.展开更多
Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.Howev...Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.However,simply adjusting weights and imposing hard constraints may not always lead to better learning of the initial conditions;sometimes it even makes it difficult for the neural networks to converge.To enhance the accuracy of PINNs in learning the initial conditions,this paper proposes a novel strategy named causally enhanced initial conditions(CEICs).This strategy works by embedding a new loss in the loss function:the loss is constructed by the derivative of the initial condition and the derivative of the neural network at the initial condition.Furthermore,to respect the causality in learning the derivative,a novel causality coefficient is introduced for the training when selecting multiple derivatives.Additionally,because CEICs can provide more accurate pseudo-labels in the first subdomain,they are compatible with the temporal-marching strategy.Experimental results demonstrate that CEICs outperform hard constraints and improve the overall accuracy of pre-training PINNs.For the 1D-Korteweg–de Vries,reaction and convection equations,the CEIC method proposed in this paper reduces the relative error by at least 60%compared to the previous methods.展开更多
Objective Vitamin deficiencies,particularly in vitamins A,B12,and D,are prevalent across populations and contribute significantly to a range of health issues.While these deficiencies are well documented,the underlying...Objective Vitamin deficiencies,particularly in vitamins A,B12,and D,are prevalent across populations and contribute significantly to a range of health issues.While these deficiencies are well documented,the underlying etiology remains complex.Recent studies suggest a close link between the gut microbiota and the synthesis,absorption,and metabolism of these vitamins.However,the specific causal relationships between the gut microbiota composition and vitamin deficiencies remain poorly understood.Identifying key bacterial species and understanding their role in vitamin metabolism could provide critical insights for targeted interventions.Methods We conducted a two-sample Mendelian randomization(MR)study to assess the causal relationship between the gut microbiota and vitamin deficiencies(A,B12,D).The genome-wide association study data for vitamin deficiencies were sourced from the FinnGen biobank,and the gut microbiota data were from the MiBioGen consortium.MR analyses included inverse variance-weighted(IVW),MR‒Egger,weighted median,and weighted mode approaches.Sensitivity analyses and reverse causality assessments were performed to ensure robustness and validate the findings.Results After FDR adjustment,vitamin B12 deficiency was associated with the class Verrucomicrobiae,order Verrucomicrobiales,family Verrucomicrobiaceae,and genus Akkermansia.Vitamin A deficiency was associated with the phylum Firmicutes and the genera Fusicatenibacter and Ruminiclostridium 6.Additional associations for vitamin B12 deficiency included the Enterobacteriaceae and Rhodospirillaceae and the genera Coprococcus 2,Lactococcus,and Ruminococcaceae UCG002.Vitamin D deficiency was associated with the genera Allisonella,Eubacterium,and Tyzzerella 3.Lachnospiraceae and Lactococcus were common risk factors for both B12 and D deficiency.Sensitivity analyses confirmed the robustness of the findings against heterogeneity and horizontal pleiotropy,and reverse MR tests indicated no evidence of reverse causality.Conclusions Our findings reveal a possible causal relationship between specific gut microbiota characteristics and vitamin A,B12 and D deficiencies,providing a theoretical basis for addressing these nutritional deficiencies through the modulation of the gut microbiota in the future and laying the groundwork for related interventions.展开更多
Associations of per-and polyfluoroalkyl substances(PFAS)on lipid metabolism have been documented but research remains scarce regarding effect of PFAS on lipid variability.To deeply understand their relationship,a step...Associations of per-and polyfluoroalkyl substances(PFAS)on lipid metabolism have been documented but research remains scarce regarding effect of PFAS on lipid variability.To deeply understand their relationship,a step-forward in causal inference is expected.To address these,we conducted a longitudinal study with three repeated measurements involving 201 participants in Beijing,among which 100 eligible participants were included for the present study.Twenty-three PFAS and four lipid indicators were assessed at each visit.We used linear mixed models and quantile g-computation models to investigate associations between PFAS and blood lipid levels.A latent class growth model described PFAS serum exposure patterns,and a generalized linear model demonstrated associations between these patterns and lipid variability.Our study found that PFDA was associated with increased TC(β=0.083,95%CI:0.011,0.155)and HDL-C(β=0.106,95%CI:0.034,0.178).The PFAS mixture also showed a positive relationship with TC(β=0.06,95%CI:0.02,0.10),with PFDA contributing most positively.Compared to the low trajectory group,the middle trajectory group for PFDA was associated with VIM of TC(β=0.756,95%CI:0.153,1.359).Furthermore,PFDA showed biological gradientswith lipid metabolism.This is the first repeated-measures study to identify the impact of PFAS serum exposure pattern on the lipid metabolism and the first to estimate the association between PFAS and blood lipid levels in middle-aged and elderly Chinese and reinforce the evidence of their causal relationship through epidemiological studies.展开更多
Osteoporosis is a known risk factor for rotator cuff tears(RCTs),but the causal correlation and underlying mechanisms remain unclear.This study aims to evaluate the impact of osteoporosis on RCT risk and investigate t...Osteoporosis is a known risk factor for rotator cuff tears(RCTs),but the causal correlation and underlying mechanisms remain unclear.This study aims to evaluate the impact of osteoporosis on RCT risk and investigate their genetic associations.Using data from the UK Biobank(n=457871),cross-sectional analyses demonstrated that osteoporosis was significantly associated with an increased risk of RCTs(adjusted OR[95%CI]=1.38[1.25–1.52]).A longitudinal analysis of a subset of patients(n=268117)over 11 years revealed that osteoporosis increased the risk of RCTs(adjusted HR[95%CI]=1.56[1.29–1.87]),which is notably varied between sexes in sex-stratified analysis.Causal inference methods,including propensity score matching,inverse probability weighting,causal random forest and survival random forest models further confirmed the causal effect,both from cross-sectional and longitudinal perspectives.展开更多
Background Although studies in recent years have explored the impact of gut microbiota on various sleep characteristics,the interaction between gut microbiota and insomnia remains unclear.Aims We aimed to evaluate the...Background Although studies in recent years have explored the impact of gut microbiota on various sleep characteristics,the interaction between gut microbiota and insomnia remains unclear.Aims We aimed to evaluate the mutual influences between gut microbiota and insomnia.Methods We conducted Mendelian randomisation(MR)analysis using genome-wide association studies datasets on insomnia(N=386533),gut microbiota data from the MiBioGen alliance(N=18340)and the Dutch Microbiome Project(N=8208).The inverse variance weighted(IVW)technique was selected as the primary approach.Then,Cochrane’s Q,Mendelian randomization-Egger(MR-Egger)and MR Pleiotropy RESidual Sum and Outlier test(MRPRESSO)tests were used to detect heterogeneity and pleiotropy.The leave-one-out method was used to test the stability of the MR results.In addition,we performed the Steiger test to thoroughly verify the causation.Results According to IVW,our results showed that 14 gut bacterial taxa may contribute to the risks of insomnia(odds ratio(OR):1.01 to 1.04),while 8 gut bacterial taxa displayed a protective effect on this condition(OR:0.97 to 0.99).Conversely,reverse MR analysis showed that insomnia may causally decrease the abundance of 7 taxa(OR:0.21 to 0.57)and increase the abundance of 12 taxa(OR:1.65 to 4.43).Notably,the genus Odoribacter showed a significant positive causal relationship after conducting the Steiger test.Cochrane’s Q test indicated no significant heterogeneity between most singlenucleotide polymorphisms.In addition,no significant level of pleiotropy was found according to MR-Egger and MRPRESSO.Conclusions Our study highlighted the reciprocal relationships between gut microbiota and insomnia,which may provide new insights into the treatment and prevention of insomnia.展开更多
基金supported by the Beijing Natural Science Foundation(No.9244019)the National Natural Science Foundation of China(Nos.72301153,62472306,and 62441221)the National Social Science Foundation of China(No.24BJY192).
文摘Currently,various rapidly developing information technologies are gradually transforming traditional social systems into Complex Social Systems(CSS).On the one hand,individuals'ability to make decisions and access information is increasing,making their behaviors more unpredictable.On the other hand,technology is facilitating an increase in the intensity and scope of individual interactions,with cascade effects making the outcomes of interactions difficult to estimate.To improve the performance of CSS,it is essential to examine the causal laws that determine what kind of performance the system exhibits.However,researches on the causal laws of CSS remain scarce,leading to the lack of foundations for analyzing such systems.Inspired by computational experiments and causal analysis,this paper proposes a Hierarchical Causal Model(HCM)with three layers,each of which presents,extracts,and applies the causality.We apply the proposed model to enhance the system performance in a typical CSS,a software-enabled small-scale plant.Experimental results show that 98.38%of the working days have better system performance than the actual performance after applying our proposed model,and the mean of the median improvement reaches 41.38%.These results validate the proposed model,demonstrating that this work provides a feasible method for the analysis of CSS.
基金funded by the Hunan Provincial Natural Science Foundation of China(Grant No.2025JJ70105)the Hunan Provincial College Students’Innovation and Entrepreneurship Training Program(Project No.S202411342056)The article processing charge(APC)was funded by the Project No.2025JJ70105.
文摘With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.
基金supported by the National Natural Sciences Foundation of China(61473056,61533005,61522304,61603068,U1560102)
文摘Rational use of blast furnace gas(BFG) in steel industry can raise economic profit, save fossil energy resources and alleviate the environment pollution. In this paper, a causality diagram is established to describe the causal relationships among the decision objective and the variables of the scheduling process for the industrial system, based on which the total scheduling amount of the BFG system can be computed by using a causal fuzzy C-means(CFCM) clustering algorithm. In this algorithm,not only the distances among the historical samples but also the effects of different solutions on the gas tank level are considered.The scheduling solution can be determined based on the proposed causal probability of the causality diagram calculated by the total amount and the conditions of the adjustable units. The causal probability quantifies the impact of different allocation schemes of the total scheduling amount on the BFG system. An evaluation method is then proposed to evaluate the effectiveness of the scheduling solutions. The experiments by using the practical data coming from a steel plant in China indicate that the proposed approach can effectively improve the scheduling accuracy and reduce the gas diffusion.
基金Supported by the Natural Science Foundation of China (No. 59677009) the National Research Foundation for the Doctoral Program of Higher Education of China (No.99061116)
文摘Causality Diagram (CD) is a new graphical knowledge representation based on probability theory. The application of this methodology in the safety analysis of the gas explosion in collieries was discussed in this paper, and the Minimal Cut Set, the Minimal Path Set and the Importance were introduced to develop the methodology. These concepts are employed to analyze the influence each event has on the top event ? the gas explosion, so as to find out about the defects of the system and accordingly help to work out the emphasis of the precautionary work and some preventive measures as well. The results of the safety analysis are in accordance with the practical requirements; therefore the preventive measures are certain to work effectively. In brief, according to the research CD is so effective in the safety analysis and the safety assessment that it can be a qualitative and quantitative method to predict the accident as well as offer some effective measures for the investigation, the prevention and the control of the accident.
基金Supported by the National Natural Science Foundation of China under Grant Nos.7110317971102129+1 种基金11121403by Program for Young Innovative Research Team in China University of Political Science and Law
文摘The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007-2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance.
基金Supported by Gansu Porvincinl Youth Scicnce Foudation
文摘The Causal relation and whole-part relation are the two fundamen-tal relations in economics.In this paper,on the basis of economic prob-lems analysis in practioce,some causal relation structures of economic sys-tems and fundamental rules for operations research are,at first,prop-osed.And then,a quotienting(simplifying)analysis approach of eco-nomic causal relations is presented and discussed in detail.At last,animprovement framework of system dynamics is proposed based on theviewpoint of generalized causal relations analysis.
基金supported by the Natural Science Foundation of China(Nos.U22A2099,62273113,62203461,62203365)the Innovation Project of Guangxi Graduate Education under Grant YCBZ2023130by the Guangxi Higher Education Undergraduate Teaching Reform Project Key Project,grant number 2022JGZ130.
文摘The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitations:1)unrealistic assumptions of complete evidence independence,and 2)a lack of mechanisms to differentiate causal relationships from spurious correlations.Existing similarity-based approaches often misinterpret interdependent evidence,leading to unreliable decision outcomes.To address these gaps,this study proposes a causality-enhanced ER rule(CER-e)framework with three key methodological innovations:1)a multidimensional causal representation of evidence to capture dependency structures;2)probabilistic quantification of causal strength using transfer entropy,a model-free information-theoretic measure;3)systematic integration of causal parameters into the ER inference process while maintaining evidential objectivity.The PC algorithm is employed during causal discovery to eliminate spurious correlations,ensuring robust causal inference.Case studies in two types of domains—telecommunications network security assessment and structural risk evaluation—validate CER-e’s effectiveness in real-world scenarios.Under simulated incomplete information conditions,the framework demonstrates superior algorithmic robustness compared to traditional ER.Comparative analyses show that CER-e significantly improves both the interpretability of causal relationships and the reliability of assessment results,establishing a novel paradigm for integrating causal inference with evidential reasoning in complex system evaluation.
文摘Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(ML)nature,causal models hope to tie the cause(s)to the effect(s)pertaining to a phenomenon(i.e.,data generating process)through causal principles.This paper presents one of the first works at creating causal models in the area of structural and construction engineering.To this end,this paper starts with a brief review of the principles of causality and then adopts four causal discovery algorithms,namely,PC(Peter-Clark),FCI(fast causal inference),GES(greedy equivalence search),and GRa SP(greedy relaxation of the sparsest permutation),have been used to examine four phenomena,including predicting the load-bearing capacity of axially loaded members,fire resistance of structural members,shear strength of beams,and resistance of walls against impulsive(blast)loading.Findings from this study reveal the possibility and merit of discovering complete and partial causal models.Finally,this study also proposes two simple metrics that can help assess the performance of causal discovery algorithms.
基金funded by the National Natural Science Foundation of China(Grant Nos.82220108002 to F.C.,82273737 to R.Z.,82473728 to Y.W.)the US National Institutes of Health(Grant Nos.CA209414,HL060710,ES000002 to D.C.C.,CA209414,CA249096 to Y.L.)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).R.Z.was partially supported by the Qing Lan Project of the Higher Education Institutions of Jiangsu Province and the Outstanding Young-Level Academic Leadership Training Program of Nanjing Medical University.
文摘Emerging evidence highlights the role of thyroid hormones in cancer,although findings are controversial.Research on thyroid-related traits in lung carcinogenesis is limited.Using UK Biobank data,we performed bidirectional Mendelian randomization(MR)to assess causal associations between lung cancer risk and thyroid dysfunction(hypothyroidism and hyperthyroidism)or functional traits(free thyroxine[FT4]and normal-range thyroid-stimulating hormone[TSH]).Furthermore,in the smoking-behavior-stratified MR analysis,we evaluated the mediating effect of thyroid-related phenotypes on the association between smoking behaviors and lung cancer.We demonstrated significant associations between lung cancer risk and hypothyroidism(hazard ratio[HR]=1.14,95%confidence interval[CI]=1.03–1.26,P=0.009)and hyperthyroidism(HR=1.55,95%CI=1.29–1.87,P=1.90×10^(-6))in the UKB.Moreover,the MR analysis indicated a causal effect of thyroid dysfunction on lung cancer risk(ORinverse variance weighted[IVW]=1.09,95%CI=1.05–1.13,P=3.12×10^(-6)for hypothyroidism;ORIVW=1.08,95%CI=1.04–1.12,P=8.14×10^(-5)for hyperthyroidism).We found that FT4 levels were protective against lung cancer risk(ORIVW=0.93,95%CI=0.87–0.99,P=0.030).Additionally,the stratified MR analysis demonstrated distinct causal effects of thyroid dysfunction on lung cancer risk among smokers.Hyperthyroidism mediated the effect of smoking behaviors,especially the age of smoking initiation(17.66%mediated),on lung cancer risk.Thus,thyroid dysfunction phenotypes play causal roles in lung cancer development exclusively among smokers and act as mediators in the causal pathway from smoking to lung cancer.
基金by grants from the Jiangsu Province 333 High-level Talent Training Project(Grant No.LGY2016010)the Nanjing Science and Technology Development Plan(Grant No.201715003)the Jiangsu Province Six Talent Peaks(Grant No.WSN-030).
文摘Dear Editor,Observational studies in epidemiology have identified a correlation between hypothyroidism and cholelithiasis[1–2].However,the causal relationship between the two diseases remains unclear.To investigate the potential causal relationship,we employed a two-sample bidirectional Mendelian randomization(MR)analysis.
基金supported by the National MCF Energy Research and Development Program(Grant Nos.2024YFE03190001,2024YFE03190004,2022YFE03030001,and 2019YFE03030002)the National Natural Science Foundation of China(Grant Nos.12405257,12475215,and 12475219)+2 种基金the Natural Science Foundation of Sichuan Province,China(Grant Nos.2023NSFSC1289 and 2025ZNSFSC0066)the Nuclear Technology Research and Development Program(Grant No.HJSYF2024(02))the Innovation Program of Southwestern Institute of Physics(Grant No.202301XWCX001)。
文摘The isotope effect on zonal flows(ZFs)and turbulence remains a key issue that is not completely solved in fusion plasmas.This paper presents the first experimental results of the ab initio prediction of causal relation between geodesic acoustic mode(GAM)and ambient turbulence at different isotope masses in the edge of HL-2A tokamak,where transfer entropy method based on information-theoretical approach is utilized as a quantified indicator of causality.Analysis shows that GAM is more pronounced in deuterium plasmas than in hydrogen,leading to a lower heat transport as well as more peaked profiles in the former situation.The causal impact of GAM on conductive heat flux component is stronger than on the convective component,which is resulted from a larger causal influence of zonal flow on temperature fluctuation.While a stronger GAM in deuterium plasmas has larger influence on all flux components,the relative change in temperature fluctuation and coefficient is more obvious when the ion mass varies.These findings not only offer an in-depth understanding of the real causality between zonal flow and turbulence in the present isotope experiments,but also provide useful ways for the physical understandings of transport and zonal flow dynamics in future deuterium-tritium fusion plasmas.
基金the financial support by the National Natural Science Foundation of China(Grant No.U24B2029)the Key Projects of the National Natural Science Foundation of China(Grant No.52334001)+1 种基金the Strategic Cooperation Technology Projects of CNPC and CUPB(Grand No.ZLZX2020-02)the China University of Petroleum,Beijing(Grand No.ZX20230042)。
文摘Offshore drilling costs are high,and the downhole environment is even more complex.Improving the rate of penetration(ROP)can effectively shorten offshore drilling cycles and improve economic benefits.It is difficult for the current ROP models to guarantee the prediction accuracy and the robustness of the models at the same time.To address the current issues,a new ROP prediction model was developed in this study,which considers ROP as a time series signal(ROP signal).The model is based on the time convolutional network(TCN)framework and integrates ensemble empirical modal decomposition(EEMD)and Bayesian network causal inference(BN),the model is named EEMD-BN-TCN.Within the proposed model,the EEMD decomposes the original ROP signal into multiple sets of sub-signals.The BN determines the causal relationship between the sub-signals and the key physical parameters(weight on bit and revolutions per minute)and carries out preliminary reconstruction of the sub-signals based on the causal relationship.The TCN predicts signals reconstructed by BN.When applying this model to an actual production well,the average absolute percentage error of the EEMD-BN-TCN prediction decreased from 18.4%with TCN to 9.2%.In addition,compared with other models,the EEMD-BN-TCN can improve the decomposition signal of ROP by regulating weight on bit and revolutions per minute,ultimately enhancing ROP.
基金National Key Research and Development Program,No.2021xjkk0303。
文摘Understanding the characteristics and driving factors behind changes in vegetation ecosystem resilience is crucial for mitigating both current and future impacts of climate change. Despite recent advances in resilience research, significant knowledge gaps remain regarding the drivers of resilience changes. In this study, we investigated the dynamics of ecosystem resilience across China and identified potential driving factors using the kernel normalized difference vegetation index(kNDVI) from 2000 to 2020. Our results indicate that vegetation resilience in China has exhibited an increasing trend over the past two decades, with a notable breakpoint occurring around 2012. We found that precipitation was the dominant driver of changes in ecosystem resilience, accounting for 35.82% of the variation across China, followed by monthly average maximum temperature(Tmax) and vapor pressure deficit(VPD), which explained 28.95% and 28.31% of the variation, respectively. Furthermore, we revealed that daytime and nighttime warming has asymmetric impacts on vegetation resilience, with temperature factors such as Tmin and Tmax becoming more influential, while the importance of precipitation slightly decreases after the resilience change point. Overall, our study highlights the key roles of water availability and temperature in shaping vegetation resilience and underscores the asymmetric effects of daytime and nighttime warming on ecosystem resilience.
基金National Key Research and Development Program of China,Grant/Award Number:2020YFB1711704。
文摘The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,directly predicting missing components via global characteristics extracted from incomplete inputs.However,this makes detail re-covery challenging,as global characteristics fail to provide complete missing component specifics.A new point cloud completion method named Point-PC is proposed.A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion.Concretely,a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format.The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain,enabling retrieval of analogous shapes from incomplete inputs.In addition,the authors employ backdoor adjustment to eliminate confounders,which are shape prior components sharing identical semantic structures with incomplete inputs.Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches.The code for Point-PC can be accessed by https://github.com/bizbard/Point-PC.git.
基金supported by the National Social Science Fund of China(2022-SKJJ-B-084).
文摘The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to falling into local optima.To address these issues,an improved GA with domain knowledge(IGADK)is proposed.Firstly,domain knowledge is incorporated into the learning process of causality to construct a new fitness function.Secondly,a dynamical mutation operator is introduced in the algorithm to accelerate the convergence rate.Finally,an experiment is conducted on simulation data,which compares the classical GA with IGADK with domain knowledge of varying accuracy.The IGADK can greatly reduce the number of iterations,populations,and samples required for learning,which illustrates the efficiency and effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(Grant Nos.1217211 and 12372244).
文摘Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.However,simply adjusting weights and imposing hard constraints may not always lead to better learning of the initial conditions;sometimes it even makes it difficult for the neural networks to converge.To enhance the accuracy of PINNs in learning the initial conditions,this paper proposes a novel strategy named causally enhanced initial conditions(CEICs).This strategy works by embedding a new loss in the loss function:the loss is constructed by the derivative of the initial condition and the derivative of the neural network at the initial condition.Furthermore,to respect the causality in learning the derivative,a novel causality coefficient is introduced for the training when selecting multiple derivatives.Additionally,because CEICs can provide more accurate pseudo-labels in the first subdomain,they are compatible with the temporal-marching strategy.Experimental results demonstrate that CEICs outperform hard constraints and improve the overall accuracy of pre-training PINNs.For the 1D-Korteweg–de Vries,reaction and convection equations,the CEIC method proposed in this paper reduces the relative error by at least 60%compared to the previous methods.
文摘Objective Vitamin deficiencies,particularly in vitamins A,B12,and D,are prevalent across populations and contribute significantly to a range of health issues.While these deficiencies are well documented,the underlying etiology remains complex.Recent studies suggest a close link between the gut microbiota and the synthesis,absorption,and metabolism of these vitamins.However,the specific causal relationships between the gut microbiota composition and vitamin deficiencies remain poorly understood.Identifying key bacterial species and understanding their role in vitamin metabolism could provide critical insights for targeted interventions.Methods We conducted a two-sample Mendelian randomization(MR)study to assess the causal relationship between the gut microbiota and vitamin deficiencies(A,B12,D).The genome-wide association study data for vitamin deficiencies were sourced from the FinnGen biobank,and the gut microbiota data were from the MiBioGen consortium.MR analyses included inverse variance-weighted(IVW),MR‒Egger,weighted median,and weighted mode approaches.Sensitivity analyses and reverse causality assessments were performed to ensure robustness and validate the findings.Results After FDR adjustment,vitamin B12 deficiency was associated with the class Verrucomicrobiae,order Verrucomicrobiales,family Verrucomicrobiaceae,and genus Akkermansia.Vitamin A deficiency was associated with the phylum Firmicutes and the genera Fusicatenibacter and Ruminiclostridium 6.Additional associations for vitamin B12 deficiency included the Enterobacteriaceae and Rhodospirillaceae and the genera Coprococcus 2,Lactococcus,and Ruminococcaceae UCG002.Vitamin D deficiency was associated with the genera Allisonella,Eubacterium,and Tyzzerella 3.Lachnospiraceae and Lactococcus were common risk factors for both B12 and D deficiency.Sensitivity analyses confirmed the robustness of the findings against heterogeneity and horizontal pleiotropy,and reverse MR tests indicated no evidence of reverse causality.Conclusions Our findings reveal a possible causal relationship between specific gut microbiota characteristics and vitamin A,B12 and D deficiencies,providing a theoretical basis for addressing these nutritional deficiencies through the modulation of the gut microbiota in the future and laying the groundwork for related interventions.
基金supported by the National Natural Science Foundation of China(No.82404365)the Noncommunicable Chronic Diseases-National Science and Technology Major Project(No.2023ZD0513200)+7 种基金China Medical Board(No.15-230)China Postdoctoral Science Foundation(Nos.2023M730317and 2023T160066)the Fundamental Research Funds for the Central Universities(No.3332023042)the Open Project of Hebei Key Laboratory of Environment and Human Health(No.202301)the National Key Research and Development Program of China(No.2022YFC3703000)the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences(No.2022-JKCS-11)the CAMS Innovation Fund for Medical Sciences(No.2022-I2M-JB-003)the Programs of the National Natural Science Foundation of China(No.21976050).
文摘Associations of per-and polyfluoroalkyl substances(PFAS)on lipid metabolism have been documented but research remains scarce regarding effect of PFAS on lipid variability.To deeply understand their relationship,a step-forward in causal inference is expected.To address these,we conducted a longitudinal study with three repeated measurements involving 201 participants in Beijing,among which 100 eligible participants were included for the present study.Twenty-three PFAS and four lipid indicators were assessed at each visit.We used linear mixed models and quantile g-computation models to investigate associations between PFAS and blood lipid levels.A latent class growth model described PFAS serum exposure patterns,and a generalized linear model demonstrated associations between these patterns and lipid variability.Our study found that PFDA was associated with increased TC(β=0.083,95%CI:0.011,0.155)and HDL-C(β=0.106,95%CI:0.034,0.178).The PFAS mixture also showed a positive relationship with TC(β=0.06,95%CI:0.02,0.10),with PFDA contributing most positively.Compared to the low trajectory group,the middle trajectory group for PFDA was associated with VIM of TC(β=0.756,95%CI:0.153,1.359).Furthermore,PFDA showed biological gradientswith lipid metabolism.This is the first repeated-measures study to identify the impact of PFAS serum exposure pattern on the lipid metabolism and the first to estimate the association between PFAS and blood lipid levels in middle-aged and elderly Chinese and reinforce the evidence of their causal relationship through epidemiological studies.
基金the Scientific Research Innovation Capability Support Project for Young Faculty(ZYGXQNJSKYCXNLZCXM-H8)Fundamental Research Funds for the Central Universities(2024ZYGXZR077)+3 种基金Guangdong Basic and Applied Basic Research Foundation(2023B1515120006)Guangzhou Basic and Applied Basic Research Foundation(2024A04J5776)the Research Fund(2023QN10Y421)Guangzhou Talent Recruitment Team Program(2024D03J0004),all related to this study.
文摘Osteoporosis is a known risk factor for rotator cuff tears(RCTs),but the causal correlation and underlying mechanisms remain unclear.This study aims to evaluate the impact of osteoporosis on RCT risk and investigate their genetic associations.Using data from the UK Biobank(n=457871),cross-sectional analyses demonstrated that osteoporosis was significantly associated with an increased risk of RCTs(adjusted OR[95%CI]=1.38[1.25–1.52]).A longitudinal analysis of a subset of patients(n=268117)over 11 years revealed that osteoporosis increased the risk of RCTs(adjusted HR[95%CI]=1.56[1.29–1.87]),which is notably varied between sexes in sex-stratified analysis.Causal inference methods,including propensity score matching,inverse probability weighting,causal random forest and survival random forest models further confirmed the causal effect,both from cross-sectional and longitudinal perspectives.
文摘Background Although studies in recent years have explored the impact of gut microbiota on various sleep characteristics,the interaction between gut microbiota and insomnia remains unclear.Aims We aimed to evaluate the mutual influences between gut microbiota and insomnia.Methods We conducted Mendelian randomisation(MR)analysis using genome-wide association studies datasets on insomnia(N=386533),gut microbiota data from the MiBioGen alliance(N=18340)and the Dutch Microbiome Project(N=8208).The inverse variance weighted(IVW)technique was selected as the primary approach.Then,Cochrane’s Q,Mendelian randomization-Egger(MR-Egger)and MR Pleiotropy RESidual Sum and Outlier test(MRPRESSO)tests were used to detect heterogeneity and pleiotropy.The leave-one-out method was used to test the stability of the MR results.In addition,we performed the Steiger test to thoroughly verify the causation.Results According to IVW,our results showed that 14 gut bacterial taxa may contribute to the risks of insomnia(odds ratio(OR):1.01 to 1.04),while 8 gut bacterial taxa displayed a protective effect on this condition(OR:0.97 to 0.99).Conversely,reverse MR analysis showed that insomnia may causally decrease the abundance of 7 taxa(OR:0.21 to 0.57)and increase the abundance of 12 taxa(OR:1.65 to 4.43).Notably,the genus Odoribacter showed a significant positive causal relationship after conducting the Steiger test.Cochrane’s Q test indicated no significant heterogeneity between most singlenucleotide polymorphisms.In addition,no significant level of pleiotropy was found according to MR-Egger and MRPRESSO.Conclusions Our study highlighted the reciprocal relationships between gut microbiota and insomnia,which may provide new insights into the treatment and prevention of insomnia.
