It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problemat...It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problematic,since there is a need to differentiate between these two scenarios.Until recently,the proper−semantic−causality of the relationship could have been determined only by human experts from the area of expertise of the studied data.This has changed with the advance of large language models,which are often utilized as surrogates for such human experts,making the process automated and readily available to all data analysts.This motivates the main objective of this work,which is to introduce the design and implementation of a large language model-based semantic causality evaluator based on correlation analysis,together with its visual analysis model called Causal heatmap.After the implementation itself,the model is evaluated from the point of view of the quality of the visual model,from the point of view of the quality of causal evaluation based on large language models,and from the point of view of comparative analysis,while the results reached in the study highlight the usability of large language models in the task and the potential of the proposed approach in the analysis of unknown datasets.The results of the experimental evaluation demonstrate the usefulness of the Causal heatmap method,supported by the evident highlighting of interesting relationships,while suppressing irrelevant ones.展开更多
AIM:To clarify the clinical correlations and causal relationships between lipid metabolism and the progression of thyroid-associated ophthalmopathy(TAO).METHODS:This case-control study retrieved clinical data from 201...AIM:To clarify the clinical correlations and causal relationships between lipid metabolism and the progression of thyroid-associated ophthalmopathy(TAO).METHODS:This case-control study retrieved clinical data from 2018 to 2023.A total of 2591 patients were enrolled,including 197 patients with TAO(case group)and 2394 patients with hyperthyroidism without TAO(control group).Serum lipid parameters,including triglycerides,total cholesterol,high-density lipoprotein(HDL),low-density lipoprotein(LDL),and the HDL/total cholesterol ratio,as well as thyroid function markers,were compared between the two groups.Correlation analyses were performed to evaluate the associations between serum lipid levels and key ocular manifestations of TAO,including exophthalmos degree,clinical activity score,and disease severity.Furthermore,Mendelian randomization(MR)analysis was conducted using genome-wide association study(GWAS)datasets,with hyperthyroidism as the exposure variable and serum lipid parameters as the outcome variables,to infer the causal relationship between hyperthyroidism,lipid metabolism,and TAO progression.RESULTS:The TAO group consisted of 101 males and 96 females,while the hyperthyroidism group included 706 males and 1688 females.Compared with the control group,patients with TAO had significantly higher levels of triglycerides(1.83±1.21 vs 1.40±1.08 mmol/L,P<0.01),total cholesterol,LDL,and HDL.Correlation analysis showed that triglyceride levels were positively correlated with exophthalmos degree,whereas HDL levels were inversely correlated with exophthalmos degree.No significant associations were found between serum lipid levels and clinical activity score(P>0.1).MR analysis confirmed that hyperthyroidism exerted a causal effect in reducing serum triglycerides[inverse-variance weighting odds ratio(OR)=0.035,95%confidence interval(CI):0.01-0.12]and total cholesterol(OR=0.085,95%CI:0.02-0.34),with no evidence of horizontal pleiotropy(MR-PRESSO P>0.05).CONCLUSION:Elevated serum triglyceride levels are an independent risk factor for TAO severity,especially exophthalmos,and triglyceride metabolism is inversely regulated by thyroid function.展开更多
Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association betw...Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.展开更多
AIM:To investigate the potential causal associations between 41 inflammatory cytokines and myopia using a two-sample Mendelian randomization(MR)approach.METHODS:Publicly available genome-wide association study(GWAS)da...AIM:To investigate the potential causal associations between 41 inflammatory cytokines and myopia using a two-sample Mendelian randomization(MR)approach.METHODS:Publicly available genome-wide association study(GWAS)datasets were utilized for this two-sample MR analysis.Inflammatory cytokine-related GWAS data were extracted from The University of Bristol’s Research Data Repository,and myopia-related GWAS data were obtained from the FinnGen project.Single nucleotide polymorphisms(SNPs)associated with inflammatory cytokines were systematically selected as instrumental variables(IVs)based on three rigorous criteria:relevance,independence,and exclusion of pleiotropy.Five MR methods were employed for causal inference:the inverse-variance weighted(IVW)method as the primary analysis,supplemented by MREgger regression,weighted median estimator,simple mode,and weighted mode approaches.Sensitivity analyses were performed to evaluate the robustness of the causal estimates.RESULTS:A total of 773 myopia-associated SNPs were identified.MR analysis revealed that higher levels of macrophage inflammatory protein 1-α(MIP-1α)were associated with a 17%reduced risk of myopia[odds ratio(OR)=0.83;95%confidence interval(CI):0.69-0.99;P<0.05].In contrast,elevated levels of eotaxin(OR=1.26;95%CI:1.07-1.47;P<0.01),stromal cell-derived factor-1α(SDF-1α;OR=1.68;95%CI:1.08-2.62;P<0.05),and interleukin-2 receptor subunit alpha(IL-2Rα;OR=1.25;95%CI:1.01-1.53;P<0.05)were significantly associated with an increased risk of myopia.Sensitivity analyses confirmed the reliability of these results.CONCLUSION:This study provides evidence supporting a causal relationship between specific inflammatory cytokines and myopia.MIP-1αmay act as a protective factor against myopia,while eotaxin,SDF-1α,and IL-2Rαare potential risk factors for myopia.These findings emphasize the critical role of inflammatory pathways in the pathogenesis of myopia,offering novel insights for the development of preventive and therapeutic strategies for myopia.展开更多
Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-h...Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-harm,long-term disability,reduced productivity,and significant societal and economic burden.Despite recent advances,detecting risk from online text remains challenging due to heterogeneous language,evolving semantics,and the sequential emergence of new datasets.Effective solutions must encode clinically meaningful cues,reason about causal relations,and adapt to new domains without forgetting prior knowledge.To address these challenges,this paper presents a Continual Neuro-Symbolic Graph Learning(CNSGL)framework that unifies symbolic reasoning,causal inference,and continual learning within a single architecture.Each post is represented as a symbolic graph linking clinically relevant tags to textual content,enriched with causal edges derived from directional Point-wise Mutual Information(PMI).A two-layer Graph Convolutional Network(GCN)encodes these graphs,and a Transformer-based attention pooler aggregates node embeddings while providing interpretable tag-level importances.Continual adaptation across datasets is achieved through the Multi-Head Freeze(MH-Freeze)strategy,which freezes a shared encoder and incrementally trains lightweight task-specific heads(small classifiers attached to the shared embedding).Experimental evaluations across six diverse mental-health datasets ranging from Reddit discourse to clinical interviews,demonstrate that MH-Freeze consistently outperforms existing continual-learning baselines in both discriminative accuracy and calibration reliability.Across six datasets,MH-Freeze achieves up to 0.925 accuracy and 0.923 F1-Score,with AUPRC≥0.934 and AUROC≥0.942,consistently surpassing all continual-learning baselines.The results confirm the framework’s ability to preserve prior knowledge,adapt to domain shifts,and maintain causal interpretability,establishing CNSGL as a promising step toward robust,explainable,and lifelong mental-health risk assessment.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
In the Southern Sichuan Basin,China(SSBC),some moderate-sized seismic events(local magnitude M_(L)ranging between 4 and 5)have affected the safe production of shale gas.In this study,we used the recorded seismic data ...In the Southern Sichuan Basin,China(SSBC),some moderate-sized seismic events(local magnitude M_(L)ranging between 4 and 5)have affected the safe production of shale gas.In this study,we used the recorded seismic data from China national and temporary networks within the SSBC to obtain the relocated seismic hypocenter distribution between January 2016 and May 2017 based on the hypocenter double-difference(HypoDD)method.The statistical characteristics of microseismicity resulting from water injection in SSBC were analyzed,and the potential correlation between the event rate and statistical parameters,such as Gutenberg-Richter b-value,spatial correlation length,and fractal dimension,was quantified.Based on spatial variations of b-value and fractal dimension of event distribution,we identified two potential risk areas in the East and West of the Zhaotong shale gas block(YS108),respectively.The focal mechanism solutions(FMSs)of the observed seismic events(M_(L)>2.5)near the H7 well pad were calculated utilizing the generalized cut-and-paste(gCAP)technique combined with P-wave polarity.The FMSs’results show reverse faults,and some of them have fault planes oriented in the N-S direction,causing oblique slip movement.In addition,we also inverted the regional stress field using high-quality FMSs,revealing that the maximum principal stress(σ1)trends NW–SE and lies nearly horizontal,in agreement with the World Stress Map and borehole breakout records.Considering geological structures and regional stress distribution,the reasons for induced seismicity were mainly linked to pore pressure diffusion.Our obtained findings may provide insights for future seismic risk assessment and mitigation strategies.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
Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that ...Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that capture correlations but fail to identify causal relationships,particularly in the presence of non-linearities and confounding factors.This limits their value for informing policy and market design in the context of the energy transition.To address this gap,we propose a novel causal inference framework based on local partially linear double machine learning(DML).Our method isolates the true impact of predicted wind and solar power generation on electricity prices by controlling for high-dimensional confounders and allowing for non-linear,context-dependent effects.This represents a substantial methodological advancement over standard econometric techniques.Applying this framework to the UK electricity market over the period 2018-2024,we produce the first robust causal estimates of how renewables affect dayahead wholesale electricity prices.We find that wind power exerts a U-shaped causal effect:at low penetration levels,a 1 GWh increase reduces prices by up to£7/MWh,the effect weakens at mid-levels,and intensifies again at higher penetration.Solar power consistently reduces prices at low penetration levels,up to£9/MWh per additional GWh,but its marginal effect diminishes quickly.Importantly,the magnitude of these effects has increased over time,reflecting the growing influence of renewables on price formation as their share in the energy mix rises.These findings offer a sound empirical basis for improving the design of support schemes,refining capacity planning,and enhancing electricity market efficiency.By providing a robust causal understanding of renewable impacts,our study contributes both methodological innovation and actionable insights to guide future energy policy.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
Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information ...Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information between different domains,which makes large language models prone to spurious correlations problems when dealing with specific domains and entities.In order to solve this problem,this paper proposes a cross-domain named entity recognition method based on causal graph structure enhancement,which captures the cross-domain invariant causal structural representations between feature representations of text sequences and annotation sequences by establishing a causal learning and intervention module,so as to improve the utilization of causal structural features by the large languagemodels in the target domains,and thus effectively alleviate the false entity bias triggered by the false relevance problem;meanwhile,through the semantic feature fusion module,the semantic information of the source and target domains is effectively combined.The results show an improvement of 2.47%and 4.12%in the political and medical domains,respectively,compared with the benchmark model,and an excellent performance in small-sample scenarios,which proves the effectiveness of causal graph structural enhancement in improving the accuracy of cross-domain entity recognition and reducing false correlations.展开更多
Recently there have been two causal modelling approaches to indicative conditionals,i.e.extrapolationist(Deng&Lee,2021)and filterist(Liang&Wang,2022),although they all take an interventionist position on subju...Recently there have been two causal modelling approaches to indicative conditionals,i.e.extrapolationist(Deng&Lee,2021)and filterist(Liang&Wang,2022),although they all take an interventionist position on subjunctive conditionals.Motivated by the so-called OK pairs,they try to provide a convincing explanation of the intuition underlying the OK pairs.As far as we know,what they have done is to provide not only an explanation of the OK pairs,but also a way of distinguishing between indicative and subjunctive conditionals.Although we agree with their success in explaining the OK pairs within a causal modelling framework,we argue that their ways of distinguishing between indicative and subjunctive conditionals fail.Instead,we argue that their approaches can be used to distinguish between two readings of conditionals,the epistemic reading and the ontic reading.which can be applied to both indicative and subjunctive conditionals.We conclude by arguing that these two readings are related to two approaches to asking and answering causal questions:the“auses-of-effects"approach and the"effects-of-causes"approach.展开更多
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
Financial time series have been analyzed with a wide variety of models and approaches,some of which can forecast with great accuracy.However,most of these models,especially the machine learning ones,cannot show additi...Financial time series have been analyzed with a wide variety of models and approaches,some of which can forecast with great accuracy.However,most of these models,especially the machine learning ones,cannot show additional information for the decision maker or the financial analyst.The notion of causality is a concept that provides a more complete understanding of a problem beyond improved forecasts.In this study,we propose integrating the treatment/control concept of causality into a forecasting framework to better predict financial time series.Our results show that the proposed methodology outperforms classic econometric approaches such as ARIMA and Random Walk,as well as machine learning approaches without the proposed methodology.This improvement is statistically significant,as indicated by the Model Confidence Set test in the complete test set and quarterly analysis.展开更多
Liquid rocket engine(LRE)fault diagnosis is critical for successful space launch missions,enabling timely avoidance of safety hazards,while accurate post-failure analysis prevents subsequent economic losses.However,th...Liquid rocket engine(LRE)fault diagnosis is critical for successful space launch missions,enabling timely avoidance of safety hazards,while accurate post-failure analysis prevents subsequent economic losses.However,the complexity of LRE systems and the“black-box”nature of current deep learning-based diagnostic methods hinder interpretable fault diagnosis.This paper establishes Granger causality(GC)extraction-based component-wise multi-layer perceptron(GCMLP),achieving high fault diagnosis accuracy while leveraging GC to enhance diagnostic interpretability.First,component-wise MLP networks are constructed for distinct LRE variables to extract inter-variable GC relationships.Second,dedicated predictors are designed for each variable,leveraging historical data and GC relationships to forecast future states,thereby ensuring GC reliability.Finally,the extracted GC features are utilized for fault classification,guaranteeing feature discriminability and diagnosis accuracy.This study simulates six critical fault modes in LRE using Simulink.Based on the generated simulation data,GCMLP demonstrates superior fault localization accuracy compared to benchmark methods,validating its efficacy and robustness.展开更多
基金supported by University Grant Agency of Matej Bel University in Banská Bystrica project number UGA-14-PDS-2025.
文摘It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problematic,since there is a need to differentiate between these two scenarios.Until recently,the proper−semantic−causality of the relationship could have been determined only by human experts from the area of expertise of the studied data.This has changed with the advance of large language models,which are often utilized as surrogates for such human experts,making the process automated and readily available to all data analysts.This motivates the main objective of this work,which is to introduce the design and implementation of a large language model-based semantic causality evaluator based on correlation analysis,together with its visual analysis model called Causal heatmap.After the implementation itself,the model is evaluated from the point of view of the quality of the visual model,from the point of view of the quality of causal evaluation based on large language models,and from the point of view of comparative analysis,while the results reached in the study highlight the usability of large language models in the task and the potential of the proposed approach in the analysis of unknown datasets.The results of the experimental evaluation demonstrate the usefulness of the Causal heatmap method,supported by the evident highlighting of interesting relationships,while suppressing irrelevant ones.
基金Supported by the National Natural Science Foundation of China(No.82371104)the Natural Science Foundation of Hunan Province(No.2023JJ30851).
文摘AIM:To clarify the clinical correlations and causal relationships between lipid metabolism and the progression of thyroid-associated ophthalmopathy(TAO).METHODS:This case-control study retrieved clinical data from 2018 to 2023.A total of 2591 patients were enrolled,including 197 patients with TAO(case group)and 2394 patients with hyperthyroidism without TAO(control group).Serum lipid parameters,including triglycerides,total cholesterol,high-density lipoprotein(HDL),low-density lipoprotein(LDL),and the HDL/total cholesterol ratio,as well as thyroid function markers,were compared between the two groups.Correlation analyses were performed to evaluate the associations between serum lipid levels and key ocular manifestations of TAO,including exophthalmos degree,clinical activity score,and disease severity.Furthermore,Mendelian randomization(MR)analysis was conducted using genome-wide association study(GWAS)datasets,with hyperthyroidism as the exposure variable and serum lipid parameters as the outcome variables,to infer the causal relationship between hyperthyroidism,lipid metabolism,and TAO progression.RESULTS:The TAO group consisted of 101 males and 96 females,while the hyperthyroidism group included 706 males and 1688 females.Compared with the control group,patients with TAO had significantly higher levels of triglycerides(1.83±1.21 vs 1.40±1.08 mmol/L,P<0.01),total cholesterol,LDL,and HDL.Correlation analysis showed that triglyceride levels were positively correlated with exophthalmos degree,whereas HDL levels were inversely correlated with exophthalmos degree.No significant associations were found between serum lipid levels and clinical activity score(P>0.1).MR analysis confirmed that hyperthyroidism exerted a causal effect in reducing serum triglycerides[inverse-variance weighting odds ratio(OR)=0.035,95%confidence interval(CI):0.01-0.12]and total cholesterol(OR=0.085,95%CI:0.02-0.34),with no evidence of horizontal pleiotropy(MR-PRESSO P>0.05).CONCLUSION:Elevated serum triglyceride levels are an independent risk factor for TAO severity,especially exophthalmos,and triglyceride metabolism is inversely regulated by thyroid function.
基金supported by the Yanzhao Gold Talent Project of Hebei Province(NO.HJZD202506)。
文摘Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.
文摘AIM:To investigate the potential causal associations between 41 inflammatory cytokines and myopia using a two-sample Mendelian randomization(MR)approach.METHODS:Publicly available genome-wide association study(GWAS)datasets were utilized for this two-sample MR analysis.Inflammatory cytokine-related GWAS data were extracted from The University of Bristol’s Research Data Repository,and myopia-related GWAS data were obtained from the FinnGen project.Single nucleotide polymorphisms(SNPs)associated with inflammatory cytokines were systematically selected as instrumental variables(IVs)based on three rigorous criteria:relevance,independence,and exclusion of pleiotropy.Five MR methods were employed for causal inference:the inverse-variance weighted(IVW)method as the primary analysis,supplemented by MREgger regression,weighted median estimator,simple mode,and weighted mode approaches.Sensitivity analyses were performed to evaluate the robustness of the causal estimates.RESULTS:A total of 773 myopia-associated SNPs were identified.MR analysis revealed that higher levels of macrophage inflammatory protein 1-α(MIP-1α)were associated with a 17%reduced risk of myopia[odds ratio(OR)=0.83;95%confidence interval(CI):0.69-0.99;P<0.05].In contrast,elevated levels of eotaxin(OR=1.26;95%CI:1.07-1.47;P<0.01),stromal cell-derived factor-1α(SDF-1α;OR=1.68;95%CI:1.08-2.62;P<0.05),and interleukin-2 receptor subunit alpha(IL-2Rα;OR=1.25;95%CI:1.01-1.53;P<0.05)were significantly associated with an increased risk of myopia.Sensitivity analyses confirmed the reliability of these results.CONCLUSION:This study provides evidence supporting a causal relationship between specific inflammatory cytokines and myopia.MIP-1αmay act as a protective factor against myopia,while eotaxin,SDF-1α,and IL-2Rαare potential risk factors for myopia.These findings emphasize the critical role of inflammatory pathways in the pathogenesis of myopia,offering novel insights for the development of preventive and therapeutic strategies for myopia.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00518960)in part by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00563192).
文摘Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-harm,long-term disability,reduced productivity,and significant societal and economic burden.Despite recent advances,detecting risk from online text remains challenging due to heterogeneous language,evolving semantics,and the sequential emergence of new datasets.Effective solutions must encode clinically meaningful cues,reason about causal relations,and adapt to new domains without forgetting prior knowledge.To address these challenges,this paper presents a Continual Neuro-Symbolic Graph Learning(CNSGL)framework that unifies symbolic reasoning,causal inference,and continual learning within a single architecture.Each post is represented as a symbolic graph linking clinically relevant tags to textual content,enriched with causal edges derived from directional Point-wise Mutual Information(PMI).A two-layer Graph Convolutional Network(GCN)encodes these graphs,and a Transformer-based attention pooler aggregates node embeddings while providing interpretable tag-level importances.Continual adaptation across datasets is achieved through the Multi-Head Freeze(MH-Freeze)strategy,which freezes a shared encoder and incrementally trains lightweight task-specific heads(small classifiers attached to the shared embedding).Experimental evaluations across six diverse mental-health datasets ranging from Reddit discourse to clinical interviews,demonstrate that MH-Freeze consistently outperforms existing continual-learning baselines in both discriminative accuracy and calibration reliability.Across six datasets,MH-Freeze achieves up to 0.925 accuracy and 0.923 F1-Score,with AUPRC≥0.934 and AUROC≥0.942,consistently surpassing all continual-learning baselines.The results confirm the framework’s ability to preserve prior knowledge,adapt to domain shifts,and maintain causal interpretability,establishing CNSGL as a promising step toward robust,explainable,and lifelong mental-health risk assessment.
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 Science Foundation of China(Grant No.U24B2038)Scientific and technological research projects in Sichuan province(Grant Nos.2024YFHZ0286and2025NSFTD0012).
文摘In the Southern Sichuan Basin,China(SSBC),some moderate-sized seismic events(local magnitude M_(L)ranging between 4 and 5)have affected the safe production of shale gas.In this study,we used the recorded seismic data from China national and temporary networks within the SSBC to obtain the relocated seismic hypocenter distribution between January 2016 and May 2017 based on the hypocenter double-difference(HypoDD)method.The statistical characteristics of microseismicity resulting from water injection in SSBC were analyzed,and the potential correlation between the event rate and statistical parameters,such as Gutenberg-Richter b-value,spatial correlation length,and fractal dimension,was quantified.Based on spatial variations of b-value and fractal dimension of event distribution,we identified two potential risk areas in the East and West of the Zhaotong shale gas block(YS108),respectively.The focal mechanism solutions(FMSs)of the observed seismic events(M_(L)>2.5)near the H7 well pad were calculated utilizing the generalized cut-and-paste(gCAP)technique combined with P-wave polarity.The FMSs’results show reverse faults,and some of them have fault planes oriented in the N-S direction,causing oblique slip movement.In addition,we also inverted the regional stress field using high-quality FMSs,revealing that the maximum principal stress(σ1)trends NW–SE and lies nearly horizontal,in agreement with the World Stress Map and borehole breakout records.Considering geological structures and regional stress distribution,the reasons for induced seismicity were mainly linked to pore pressure diffusion.Our obtained findings may provide insights for future seismic risk assessment and mitigation strategies.
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.
文摘Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that capture correlations but fail to identify causal relationships,particularly in the presence of non-linearities and confounding factors.This limits their value for informing policy and market design in the context of the energy transition.To address this gap,we propose a novel causal inference framework based on local partially linear double machine learning(DML).Our method isolates the true impact of predicted wind and solar power generation on electricity prices by controlling for high-dimensional confounders and allowing for non-linear,context-dependent effects.This represents a substantial methodological advancement over standard econometric techniques.Applying this framework to the UK electricity market over the period 2018-2024,we produce the first robust causal estimates of how renewables affect dayahead wholesale electricity prices.We find that wind power exerts a U-shaped causal effect:at low penetration levels,a 1 GWh increase reduces prices by up to£7/MWh,the effect weakens at mid-levels,and intensifies again at higher penetration.Solar power consistently reduces prices at low penetration levels,up to£9/MWh per additional GWh,but its marginal effect diminishes quickly.Importantly,the magnitude of these effects has increased over time,reflecting the growing influence of renewables on price formation as their share in the energy mix rises.These findings offer a sound empirical basis for improving the design of support schemes,refining capacity planning,and enhancing electricity market efficiency.By providing a robust causal understanding of renewable impacts,our study contributes both methodological innovation and actionable insights to guide future energy policy.
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 National Natural Science Foundation of China Joint Fund for Enterprise Innovation Development(U23B2029)National Natural Science Foundation of China(62076167,61772020)+1 种基金Key Scientific Research Project of Higher Education Institutions in Henan Province(24A520058,24A520060,23A520022)Postgraduate Education Reform and Quality Improvement Project of Henan Province(YJS2024AL053).
文摘Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information between different domains,which makes large language models prone to spurious correlations problems when dealing with specific domains and entities.In order to solve this problem,this paper proposes a cross-domain named entity recognition method based on causal graph structure enhancement,which captures the cross-domain invariant causal structural representations between feature representations of text sequences and annotation sequences by establishing a causal learning and intervention module,so as to improve the utilization of causal structural features by the large languagemodels in the target domains,and thus effectively alleviate the false entity bias triggered by the false relevance problem;meanwhile,through the semantic feature fusion module,the semantic information of the source and target domains is effectively combined.The results show an improvement of 2.47%and 4.12%in the political and medical domains,respectively,compared with the benchmark model,and an excellent performance in small-sample scenarios,which proves the effectiveness of causal graph structural enhancement in improving the accuracy of cross-domain entity recognition and reducing false correlations.
基金supported by China’s MOE project of Key Research Institute of Humanities and Social Sciences at Universities(22JJD720021)the project of Shandong University(11090087395308).
文摘Recently there have been two causal modelling approaches to indicative conditionals,i.e.extrapolationist(Deng&Lee,2021)and filterist(Liang&Wang,2022),although they all take an interventionist position on subjunctive conditionals.Motivated by the so-called OK pairs,they try to provide a convincing explanation of the intuition underlying the OK pairs.As far as we know,what they have done is to provide not only an explanation of the OK pairs,but also a way of distinguishing between indicative and subjunctive conditionals.Although we agree with their success in explaining the OK pairs within a causal modelling framework,we argue that their ways of distinguishing between indicative and subjunctive conditionals fail.Instead,we argue that their approaches can be used to distinguish between two readings of conditionals,the epistemic reading and the ontic reading.which can be applied to both indicative and subjunctive conditionals.We conclude by arguing that these two readings are related to two approaches to asking and answering causal questions:the“auses-of-effects"approach and the"effects-of-causes"approach.
基金supported by the 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ANID for supporting the Fondecyt project 1200555.
文摘Financial time series have been analyzed with a wide variety of models and approaches,some of which can forecast with great accuracy.However,most of these models,especially the machine learning ones,cannot show additional information for the decision maker or the financial analyst.The notion of causality is a concept that provides a more complete understanding of a problem beyond improved forecasts.In this study,we propose integrating the treatment/control concept of causality into a forecasting framework to better predict financial time series.Our results show that the proposed methodology outperforms classic econometric approaches such as ARIMA and Random Walk,as well as machine learning approaches without the proposed methodology.This improvement is statistically significant,as indicated by the Model Confidence Set test in the complete test set and quarterly analysis.
文摘Liquid rocket engine(LRE)fault diagnosis is critical for successful space launch missions,enabling timely avoidance of safety hazards,while accurate post-failure analysis prevents subsequent economic losses.However,the complexity of LRE systems and the“black-box”nature of current deep learning-based diagnostic methods hinder interpretable fault diagnosis.This paper establishes Granger causality(GC)extraction-based component-wise multi-layer perceptron(GCMLP),achieving high fault diagnosis accuracy while leveraging GC to enhance diagnostic interpretability.First,component-wise MLP networks are constructed for distinct LRE variables to extract inter-variable GC relationships.Second,dedicated predictors are designed for each variable,leveraging historical data and GC relationships to forecast future states,thereby ensuring GC reliability.Finally,the extracted GC features are utilized for fault classification,guaranteeing feature discriminability and diagnosis accuracy.This study simulates six critical fault modes in LRE using Simulink.Based on the generated simulation data,GCMLP demonstrates superior fault localization accuracy compared to benchmark methods,validating its efficacy and robustness.
