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
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
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
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
Natural systems are typically nonlinear and complex, and it is of great interest to be able to reconstruct a system in order to understand its mechanism, which cannot only recover nonlinear behaviors but also predict ...Natural systems are typically nonlinear and complex, and it is of great interest to be able to reconstruct a system in order to understand its mechanism, which cannot only recover nonlinear behaviors but also predict future dynamics. Due to the advances of modern technology, big data becomes increasingly accessible and consequently the problem of reconstructing systems from measured data or time series plays a central role in many scientific disciplines. In recent decades, nonlinear methods rooted in state space reconstruction have been developed, and they do not assume any model equations but can recover the dynamics purely from the measured time series data. In this review, the development of state space reconstruction techniques will be introduced and the recent advances in systems prediction and causality inference using state space reconstruction will be presented. Particularly, the cutting-edge method to deal with short-term time series data will be focused on.Finally, the advantages as well as the remaining problems in this field are discussed.展开更多
It has been evidenced that peer review activities are positively correlated to scientists’bibliometric performance(e.g.,Ortega,2017,2019).However,how the number of paper’reviewing’interacts with a scientist’s’pub...It has been evidenced that peer review activities are positively correlated to scientists’bibliometric performance(e.g.,Ortega,2017,2019).However,how the number of paper’reviewing’interacts with a scientist’s’publishing’has not been addressed in previous studies.This paper attempts to employ the Granger causality inference to explore the directionality between a scientist’s publication performance and his/her review activities.Our dataset comprises scientists’reviewed articles derived from Publons in the Web of Knowledge database,and their publications retrieved from Pub Med.We find that scientists who reviewed less or published less tend to have Granger causality between reviewing and publishing activities.In addition,compared with early-career researchers,reviewing advances publishing for senior scientists.展开更多
Image classification algorithms are commonly based on the Independent and Identically Distribution (i.i.d.) assumption, but in practice, the Out-Of-Distribution (OOD) problem widely exists, that is, the contexts of im...Image classification algorithms are commonly based on the Independent and Identically Distribution (i.i.d.) assumption, but in practice, the Out-Of-Distribution (OOD) problem widely exists, that is, the contexts of images in the model predicting are usually unseen during training. In this case, existing models trained under the i.i.d. assumption are limiting generalisation. Causal inference is an important method to learn the causal associations which are invariant across different environments, thus improving the generalisation ability of the model. However, existing methods usually require partitioning of the environment to learn invariant features, which mostly have imbalance problems due to the lack of constraints. In this paper, we propose a balanced causal learning framework (BCL), starting from how to divide the dataset in a balanced way and the balance of training after the division, which automatically generates fine-grained balanced data partitions in an unsupervised manner and balances the training difficulty of different classes, thereby enhancing the generalisation ability of models in different environments. Experiments on the OOD datasets NICO and NICO++ demonstrate that BCL achieves stable predictions on OOD data, and we also find that models using BCL focus more accurately on the foreground of images compared with the existing causal inference method, which effectively improves the generalisation ability.展开更多
This study’s main purpose is to use Bayesian structural time-series models to investigate the causal effect of an earthquake on the Borsa Istanbul Stock Index.The results reveal a significant negative impact on stock...This study’s main purpose is to use Bayesian structural time-series models to investigate the causal effect of an earthquake on the Borsa Istanbul Stock Index.The results reveal a significant negative impact on stock market value during the post-treatment period.The results indicate rapid divergence from counterfactual predictions,and the actual stock index is lower than would have been expected in the absence of an earthquake.The curve of the actual stock value and the counterfactual prediction after the earthquake suggest a reconvening pattern in the stock market when the stock market resumes its activities.The cumulative impact effect shows a negative effect in relative terms,as evidenced by the decrease in the BIST-100 index of -30%.These results have significant implications for investors and policymakers,emphasizing the need to prepare for natural disasters to minimize their adverse effects on stock market valuations.展开更多
Modern industrial systems are usually in large scale,consisting of massive components and variables that form a complex system topology.Owing to the interconnections among devices,a fault may occur and propagate to ex...Modern industrial systems are usually in large scale,consisting of massive components and variables that form a complex system topology.Owing to the interconnections among devices,a fault may occur and propagate to exert widespread influences and lead to a variety of alarms.Obtaining the root causes of alarms is beneficial to the decision supports in making corrective alarm responses.Existing data-driven methods for alarm root cause analysis detect causal relations among alarms mainly based on historical alarm event data.To improve the accuracy,this paper proposes a causal fusion inference method for industrial alarm root cause analysis based on process topology and alarm events.A Granger causality inference method considering process topology is exploited to find out the causal relations among alarms.The topological nodes are used as the inputs of the model,and the alarm causal adjacency matrix between alarm variables is obtained by calculating the likelihood of the topological Hawkes process.The root cause is then obtained from the directed acyclic graph(DAG)among alarm variables.The effectiveness of the proposed method is verified by simulations based on both a numerical example and the Tennessee Eastman process(TEP)model.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
Background The annotation of fashion images is a significantly important task in the fashion industry as well as social media and e-commerce.However,owing to the complexity and diversity of fashion images,this task en...Background The annotation of fashion images is a significantly important task in the fashion industry as well as social media and e-commerce.However,owing to the complexity and diversity of fashion images,this task entails multiple challenges,including the lack of fine-grained captions and confounders caused by dataset bias.Specifically,confounders often cause models to learn spurious correlations,thereby reducing their generalization capabilities.Method In this work,we propose the Deconfounded Fashion Image Captioning(DFIC)framework,which first uses multimodal retrieval to enrich the predicted captions of clothing,and then constructs a detailed causal graph using causal inference in the decoder to perform deconfounding.Multimodal retrieval is used to obtain semantic words related to image features,which are input into the decoder as prompt words to enrich sentence descriptions.In the decoder,causal inference is applied to disentangle visual and semantic features while concurrently eliminating visual and language confounding.Results Overall,our method can not only effectively enrich the captions of target images,but also greatly reduce confounders caused by the dataset.To verify the effectiveness of the proposed framework,the model was experimentally verified using the FACAD dataset.展开更多
Causal inference is a powerful modeling tool for explanatory analysis,which might enable current machine learning to become explainable.How to marry causal inference with machine learning to develop explainable artifi...Causal inference is a powerful modeling tool for explanatory analysis,which might enable current machine learning to become explainable.How to marry causal inference with machine learning to develop explainable artificial intelligence(XAI)algorithms is one of key steps toward to the artificial intelligence 2.0.With the aim of bringing knowledge of causal inference to scholars of machine learning and artificial intelligence,we invited researchers working on causal inference to write this survey from different aspects of causal inference.This survey includes the following sections:“Estimating average treatment effect:A brief review and beyond”from Dr.Kun Kuang,“Attribution problems in counterfactual inference”from Prof.Lian Li,“The Yule–Simpson paradox and the surrogate paradox”from Prof.Zhi Geng,“Causal potential theory”from Prof.Lei Xu,“Discovering causal information from observational data”from Prof.Kun Zhang,“Formal argumentation in causal reasoning and explanation”from Profs.Beishui Liao and Huaxin Huang,“Causal inference with complex experiments”from Prof.Peng Ding,“Instrumental variables and negative controls for observational studies”from Prof.Wang Miao,and“Causal inference with interference”from Dr.Zhichao Jiang.展开更多
Regression is a widely used econometric tool in research. In observational studies, based on a number of assumptions, regression-based statistical control methods attempt to analyze the causation between treatment and...Regression is a widely used econometric tool in research. In observational studies, based on a number of assumptions, regression-based statistical control methods attempt to analyze the causation between treatment and outcome by adding control variables. However, this approach may not produce reliable estimates of causal effects. In addition to the shortcomings of the method, this lack of confidence is mainly related to ambiguous formulations in econometrics, such as the definition of selection bias, selection of core control variables, and method of testing for robustness. Within the framework of the causal models, we clarify the assumption of causal inference using regression-based statistical controls, as described in econometrics, and discuss how to select core control variables to satisfy this assumption and conduct robustness tests for regression estimates.展开更多
Causal inference prevails in the field of laparoscopic surgery.Once the causality between an intervention and outcome is established,the intervention can be applied to a target population to improve clinical outcomes....Causal inference prevails in the field of laparoscopic surgery.Once the causality between an intervention and outcome is established,the intervention can be applied to a target population to improve clinical outcomes.In many clinical scenarios,interventions are applied longitudinally in response to patients’conditions.Such longitudinal data comprise static variables,such as age,gender,and comorbidities;and dynamic variables,such as the treatment regime,laboratory variables,and vital signs.Some dynamic variables can act as both the confounder and mediator for the effect of an intervention on the outcome;in such cases,simple adjustment with a conventional regression model will bias the effect sizes.To address this,numerous statistical methods are being developed for causal inference;these include,but are not limited to,the structural marginal Cox regression model,dynamic treatment regime,and Cox regression model with time-varying covariates.This technical note provides a gentle introduction to such models and illustrates their use with an example in the field of laparoscopic surgery.展开更多
Statistical approaches for evaluating causal effects and for discovering causal networks are discussed in this paper.A causal relation between two variables is different from an association or correlation between them...Statistical approaches for evaluating causal effects and for discovering causal networks are discussed in this paper.A causal relation between two variables is different from an association or correlation between them.An association measurement between two variables and may be changed dramatically from positive to negative by omitting a third variable,which is called Yule-Simpson paradox.We shall discuss how to evaluate the causal effect of a treatment or exposure on an outcome to avoid the phenomena of Yule-Simpson paradox. Surrogates and intermediate variables are often used to reduce measurement costs or duration when measurement of endpoint variables is expensive,inconvenient,infeasible or unobservable in practice.There have been many criteria for surrogates.However,it is possible that for a surrogate satisfying these criteria,a treatment has a positive effect on the surrogate,which in turn has a positive effect on the outcome,but the treatment has a negative effect on the outcome,which is called the surrogate paradox.We shall discuss criteria for surrogates to avoid the phenomena of the surrogate paradox. Causal networks which describe the causal relationships among a large number of variables have been applied to many research fields.It is important to discover structures of causal networks from observed data.We propose a recursive approach for discovering a causal network in which a structural learning of a large network is decomposed recursively into learning of small networks.Further to discover causal relationships,we present an active learning approach in terms of external interventions on some variables.When we focus on the causes of an interest outcome, instead of discovering a whole network,we propose a local learning approach to discover these causes that affect the outcome.展开更多
BACKGROUND Despite being one of the most prevalent sleep disorders,obstructive sleep apnea hypoventilation syndrome(OSAHS)has limited information on its immunologic foundation.The immunological underpinnings of certai...BACKGROUND Despite being one of the most prevalent sleep disorders,obstructive sleep apnea hypoventilation syndrome(OSAHS)has limited information on its immunologic foundation.The immunological underpinnings of certain major psychiatric diseases have been uncovered in recent years thanks to the extensive use of genome-wide association studies(GWAS)and genotyping techniques using highdensity genetic markers(e.g.,SNP or CNVs).But this tactic hasn't yet been applied to OSAHS.Using a Mendelian randomization analysis,we analyzed the causal link between immune cells and the illness in order to comprehend the immunological bases of OSAHS.AIM To investigate the immune cells'association with OSAHS via genetic methods,guiding future clinical research.METHODS A comprehensive two-sample mendelian randomization study was conducted to investigate the causal relationship between immune cell characteristics and OSAHS.Summary statistics for each immune cell feature were obtained from the GWAS catalog.Information on 731 immune cell properties,such as morphologic parameters,median fluorescence intensity,absolute cellular,and relative cellular,was compiled using publicly available genetic databases.The results'robustness,heterogeneity,and horizontal pleiotropy were confirmed using extensive sensitivity examination.RESULTS Following false discovery rate(FDR)correction,no statistically significant effect of OSAHS on immunophenotypes was observed.However,two lymphocyte subsets were found to have a significant association with the risk of OSAHS:Basophil%CD33dim HLA DR-CD66b-(OR=1.03,95%CI=1.01-1.03,P<0.001);CD38 on IgD+CD24-B cell(OR=1.04,95%CI=1.02-1.04,P=0.019).CONCLUSION This study shows a strong link between immune cells and OSAHS through a gene approach,thus offering direction for potential future medical research.展开更多
Microblogging platforms like X(formerly Twitter)and Sina Weibo have become key channels for spreading information online.Accurately predicting information spread,such as users’reposting activities,is essential for ap...Microblogging platforms like X(formerly Twitter)and Sina Weibo have become key channels for spreading information online.Accurately predicting information spread,such as users’reposting activities,is essential for applications including content recommendation and analyzing public sentiment.Current advanced models rely on deep representation learning to extract features from various inputs,such as users’social connections and repost history,to forecast reposting behavior.Nonetheless,these models frequently ignore intrinsic confounding factors,which may cause the models to capture spurious relationships,ultimately impacting prediction performance.To address this limitation,we propose a novel Debiased Reposting Prediction model(DRP).Our model mitigates the influence of confounding variables by incorporating intervention operations from causal inference,enabling it to learn the causal associations between features and user reposting behavior.Specifically,we introduce a memory network within DRP to enhance the model’s perception of confounder distributions.This network aggregates and learns confounding information dispersed across different training data batches by optimizing the reconstruction loss.Furthermore,recognizing the challenge of acquiring prior knowledge of causal graphs,which is crucial for causal inference,we develop a causal discovery module within DRP(CD-DRP).This module allows the model to autonomously uncover the causal graph of feature variables by analyzing microblogging data.Experimental results on multiple real-world datasets demonstrate that our proposed method effectively uncovers causal relationships between variables,exhibits strong time efficiency,and outperforms state-of-the-art models in prediction performance(improved by 2.54%)and overfitting reduction(by 7.44%).展开更多
This study explores the application of Bayesian econometrics in policy evaluation through theoretical analysis. The research first reviews the theoretical foundations of Bayesian methods, including the concepts of Bay...This study explores the application of Bayesian econometrics in policy evaluation through theoretical analysis. The research first reviews the theoretical foundations of Bayesian methods, including the concepts of Bayesian inference, prior distributions, and posterior distributions. Through systematic analysis, the study constructs a theoretical framework for applying Bayesian methods in policy evaluation. The research finds that Bayesian methods have multiple theoretical advantages in policy evaluation: Based on parameter uncertainty theory, Bayesian methods can better handle uncertainty in model parameters and provide more comprehensive estimates of policy effects;from the perspective of model selection theory, Bayesian model averaging can reduce model selection bias and enhance the robustness of evaluation results;according to causal inference theory, Bayesian causal inference methods provide new approaches for evaluating policy causal effects. The study also points out the complexities of applying Bayesian methods in policy evaluation, such as the selection of prior information and computational complexity. To address these complexities, the study proposes hybrid methods combining frequentist approaches and suggestions for developing computationally efficient algorithms. The research also discusses theoretical comparisons between Bayesian methods and other policy evaluation techniques, providing directions for future research.展开更多
Deep learning relies on learning from extensive data to generate prediction results.This approach may inadvertently capture spurious correlations within the data,leading to models that lack interpretability and robust...Deep learning relies on learning from extensive data to generate prediction results.This approach may inadvertently capture spurious correlations within the data,leading to models that lack interpretability and robustness.Researchers have developed more profound and stable causal inference methods based on cognitive neuroscience.By replacing the correlation model with a stable and interpretable causal model,it is possible to mitigate the misleading nature of spurious correlations and overcome the limitations of model calculations.In this survey,we provide a comprehensive and structured review of causal inference methods in deep learning.Brain-like inference ideas are discussed from a brain-inspired perspective,and the basic concepts of causal learning are introduced.The article describes the integration of causal inference with traditional deep learning algorithms and illustrates its application to large model tasks as well as specific modalities in deep learning.The current limitations of causal inference and future research directions are discussed.Moreover,the commonly used benchmark datasets and the corresponding download links are summarized.展开更多
This study aims to conduct an in-depth analysis of social media data using causal inference methods to explore the underlying mechanisms driving user behavior patterns.By leveraging large-scale social media datasets,t...This study aims to conduct an in-depth analysis of social media data using causal inference methods to explore the underlying mechanisms driving user behavior patterns.By leveraging large-scale social media datasets,this research develops a systematic analytical framework that integrates techniques such as propensity score matching,regression analysis,and regression discontinuity design to identify the causal effects of content characteristics,user attributes,and social network structures on user interactions,including clicks,shares,comments,and likes.The empirical findings indicate that factors such as sentiment,topical relevance,and network centrality have significant causal impacts on user behavior,with notable differences observed among various user groups.This study not only enriches the theoretical understanding of social media data analysis but also provides data-driven decision support and practical guidance for fields such as digital marketing,public opinion management,and digital governance.展开更多
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.
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 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 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supported by the National Key Research and Development Program of China (Grant No. 2017YFA0505500)Japan Society for the Promotion of Science KAKENHI Program (Grant No. JP15H05707)National Natural Science Foundation of China (Grant Nos. 11771010,31771476,91530320, 91529303,91439103 and 81471047)
文摘Natural systems are typically nonlinear and complex, and it is of great interest to be able to reconstruct a system in order to understand its mechanism, which cannot only recover nonlinear behaviors but also predict future dynamics. Due to the advances of modern technology, big data becomes increasingly accessible and consequently the problem of reconstructing systems from measured data or time series plays a central role in many scientific disciplines. In recent decades, nonlinear methods rooted in state space reconstruction have been developed, and they do not assume any model equations but can recover the dynamics purely from the measured time series data. In this review, the development of state space reconstruction techniques will be introduced and the recent advances in systems prediction and causality inference using state space reconstruction will be presented. Particularly, the cutting-edge method to deal with short-term time series data will be focused on.Finally, the advantages as well as the remaining problems in this field are discussed.
文摘It has been evidenced that peer review activities are positively correlated to scientists’bibliometric performance(e.g.,Ortega,2017,2019).However,how the number of paper’reviewing’interacts with a scientist’s’publishing’has not been addressed in previous studies.This paper attempts to employ the Granger causality inference to explore the directionality between a scientist’s publication performance and his/her review activities.Our dataset comprises scientists’reviewed articles derived from Publons in the Web of Knowledge database,and their publications retrieved from Pub Med.We find that scientists who reviewed less or published less tend to have Granger causality between reviewing and publishing activities.In addition,compared with early-career researchers,reviewing advances publishing for senior scientists.
基金TaiShan Scholars Program(Grant no.tsqn202211289)National Key R&D Program of China(Grant no.2021YFC3300203)Oversea Innovation Team Project of the“20 Regulations for New Universities”funding program of Jinan(Grant no.2021GXRC073),and the Excellent Youth Scholars Program of Shandong Province(Grant no.2022HWYQ-048).
文摘Image classification algorithms are commonly based on the Independent and Identically Distribution (i.i.d.) assumption, but in practice, the Out-Of-Distribution (OOD) problem widely exists, that is, the contexts of images in the model predicting are usually unseen during training. In this case, existing models trained under the i.i.d. assumption are limiting generalisation. Causal inference is an important method to learn the causal associations which are invariant across different environments, thus improving the generalisation ability of the model. However, existing methods usually require partitioning of the environment to learn invariant features, which mostly have imbalance problems due to the lack of constraints. In this paper, we propose a balanced causal learning framework (BCL), starting from how to divide the dataset in a balanced way and the balance of training after the division, which automatically generates fine-grained balanced data partitions in an unsupervised manner and balances the training difficulty of different classes, thereby enhancing the generalisation ability of models in different environments. Experiments on the OOD datasets NICO and NICO++ demonstrate that BCL achieves stable predictions on OOD data, and we also find that models using BCL focus more accurately on the foreground of images compared with the existing causal inference method, which effectively improves the generalisation ability.
文摘This study’s main purpose is to use Bayesian structural time-series models to investigate the causal effect of an earthquake on the Borsa Istanbul Stock Index.The results reveal a significant negative impact on stock market value during the post-treatment period.The results indicate rapid divergence from counterfactual predictions,and the actual stock index is lower than would have been expected in the absence of an earthquake.The curve of the actual stock value and the counterfactual prediction after the earthquake suggest a reconvening pattern in the stock market when the stock market resumes its activities.The cumulative impact effect shows a negative effect in relative terms,as evidenced by the decrease in the BIST-100 index of -30%.These results have significant implications for investors and policymakers,emphasizing the need to prepare for natural disasters to minimize their adverse effects on stock market valuations.
基金supported by the National Natural Science Foundation of China(Nos.61903345 and 61973287)。
文摘Modern industrial systems are usually in large scale,consisting of massive components and variables that form a complex system topology.Owing to the interconnections among devices,a fault may occur and propagate to exert widespread influences and lead to a variety of alarms.Obtaining the root causes of alarms is beneficial to the decision supports in making corrective alarm responses.Existing data-driven methods for alarm root cause analysis detect causal relations among alarms mainly based on historical alarm event data.To improve the accuracy,this paper proposes a causal fusion inference method for industrial alarm root cause analysis based on process topology and alarm events.A Granger causality inference method considering process topology is exploited to find out the causal relations among alarms.The topological nodes are used as the inputs of the model,and the alarm causal adjacency matrix between alarm variables is obtained by calculating the likelihood of the topological Hawkes process.The root cause is then obtained from the directed acyclic graph(DAG)among alarm variables.The effectiveness of the proposed method is verified by simulations based on both a numerical example and the Tennessee Eastman process(TEP)model.
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.
文摘Background The annotation of fashion images is a significantly important task in the fashion industry as well as social media and e-commerce.However,owing to the complexity and diversity of fashion images,this task entails multiple challenges,including the lack of fine-grained captions and confounders caused by dataset bias.Specifically,confounders often cause models to learn spurious correlations,thereby reducing their generalization capabilities.Method In this work,we propose the Deconfounded Fashion Image Captioning(DFIC)framework,which first uses multimodal retrieval to enrich the predicted captions of clothing,and then constructs a detailed causal graph using causal inference in the decoder to perform deconfounding.Multimodal retrieval is used to obtain semantic words related to image features,which are input into the decoder as prompt words to enrich sentence descriptions.In the decoder,causal inference is applied to disentangle visual and semantic features while concurrently eliminating visual and language confounding.Results Overall,our method can not only effectively enrich the captions of target images,but also greatly reduce confounders caused by the dataset.To verify the effectiveness of the proposed framework,the model was experimentally verified using the FACAD dataset.
文摘Causal inference is a powerful modeling tool for explanatory analysis,which might enable current machine learning to become explainable.How to marry causal inference with machine learning to develop explainable artificial intelligence(XAI)algorithms is one of key steps toward to the artificial intelligence 2.0.With the aim of bringing knowledge of causal inference to scholars of machine learning and artificial intelligence,we invited researchers working on causal inference to write this survey from different aspects of causal inference.This survey includes the following sections:“Estimating average treatment effect:A brief review and beyond”from Dr.Kun Kuang,“Attribution problems in counterfactual inference”from Prof.Lian Li,“The Yule–Simpson paradox and the surrogate paradox”from Prof.Zhi Geng,“Causal potential theory”from Prof.Lei Xu,“Discovering causal information from observational data”from Prof.Kun Zhang,“Formal argumentation in causal reasoning and explanation”from Profs.Beishui Liao and Huaxin Huang,“Causal inference with complex experiments”from Prof.Peng Ding,“Instrumental variables and negative controls for observational studies”from Prof.Wang Miao,and“Causal inference with interference”from Dr.Zhichao Jiang.
基金This research was funded by the National Natural Science Foundation of China(Grant No.72074060).
文摘Regression is a widely used econometric tool in research. In observational studies, based on a number of assumptions, regression-based statistical control methods attempt to analyze the causation between treatment and outcome by adding control variables. However, this approach may not produce reliable estimates of causal effects. In addition to the shortcomings of the method, this lack of confidence is mainly related to ambiguous formulations in econometrics, such as the definition of selection bias, selection of core control variables, and method of testing for robustness. Within the framework of the causal models, we clarify the assumption of causal inference using regression-based statistical controls, as described in econometrics, and discuss how to select core control variables to satisfy this assumption and conduct robustness tests for regression estimates.
基金funding from the National Natural Science Foundation of China(82272180)Open Foundation of Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province(SZZD202206)+2 种基金funding from the Sichuan Medical Association Scientific Research Project(S21019)funding from the Key Research and Development Project of Zhejiang Province(2021C03071)funding from Zhejiang Medical and Health Science and Technology Project(2017ZD001)。
文摘Causal inference prevails in the field of laparoscopic surgery.Once the causality between an intervention and outcome is established,the intervention can be applied to a target population to improve clinical outcomes.In many clinical scenarios,interventions are applied longitudinally in response to patients’conditions.Such longitudinal data comprise static variables,such as age,gender,and comorbidities;and dynamic variables,such as the treatment regime,laboratory variables,and vital signs.Some dynamic variables can act as both the confounder and mediator for the effect of an intervention on the outcome;in such cases,simple adjustment with a conventional regression model will bias the effect sizes.To address this,numerous statistical methods are being developed for causal inference;these include,but are not limited to,the structural marginal Cox regression model,dynamic treatment regime,and Cox regression model with time-varying covariates.This technical note provides a gentle introduction to such models and illustrates their use with an example in the field of laparoscopic surgery.
文摘Statistical approaches for evaluating causal effects and for discovering causal networks are discussed in this paper.A causal relation between two variables is different from an association or correlation between them.An association measurement between two variables and may be changed dramatically from positive to negative by omitting a third variable,which is called Yule-Simpson paradox.We shall discuss how to evaluate the causal effect of a treatment or exposure on an outcome to avoid the phenomena of Yule-Simpson paradox. Surrogates and intermediate variables are often used to reduce measurement costs or duration when measurement of endpoint variables is expensive,inconvenient,infeasible or unobservable in practice.There have been many criteria for surrogates.However,it is possible that for a surrogate satisfying these criteria,a treatment has a positive effect on the surrogate,which in turn has a positive effect on the outcome,but the treatment has a negative effect on the outcome,which is called the surrogate paradox.We shall discuss criteria for surrogates to avoid the phenomena of the surrogate paradox. Causal networks which describe the causal relationships among a large number of variables have been applied to many research fields.It is important to discover structures of causal networks from observed data.We propose a recursive approach for discovering a causal network in which a structural learning of a large network is decomposed recursively into learning of small networks.Further to discover causal relationships,we present an active learning approach in terms of external interventions on some variables.When we focus on the causes of an interest outcome, instead of discovering a whole network,we propose a local learning approach to discover these causes that affect the outcome.
基金Supported by Doctoral Research Fund Project of Henan Provincial Hospital of Traditional Chinese Medicine,No.2022BSJJ10.
文摘BACKGROUND Despite being one of the most prevalent sleep disorders,obstructive sleep apnea hypoventilation syndrome(OSAHS)has limited information on its immunologic foundation.The immunological underpinnings of certain major psychiatric diseases have been uncovered in recent years thanks to the extensive use of genome-wide association studies(GWAS)and genotyping techniques using highdensity genetic markers(e.g.,SNP or CNVs).But this tactic hasn't yet been applied to OSAHS.Using a Mendelian randomization analysis,we analyzed the causal link between immune cells and the illness in order to comprehend the immunological bases of OSAHS.AIM To investigate the immune cells'association with OSAHS via genetic methods,guiding future clinical research.METHODS A comprehensive two-sample mendelian randomization study was conducted to investigate the causal relationship between immune cell characteristics and OSAHS.Summary statistics for each immune cell feature were obtained from the GWAS catalog.Information on 731 immune cell properties,such as morphologic parameters,median fluorescence intensity,absolute cellular,and relative cellular,was compiled using publicly available genetic databases.The results'robustness,heterogeneity,and horizontal pleiotropy were confirmed using extensive sensitivity examination.RESULTS Following false discovery rate(FDR)correction,no statistically significant effect of OSAHS on immunophenotypes was observed.However,two lymphocyte subsets were found to have a significant association with the risk of OSAHS:Basophil%CD33dim HLA DR-CD66b-(OR=1.03,95%CI=1.01-1.03,P<0.001);CD38 on IgD+CD24-B cell(OR=1.04,95%CI=1.02-1.04,P=0.019).CONCLUSION This study shows a strong link between immune cells and OSAHS through a gene approach,thus offering direction for potential future medical research.
文摘Microblogging platforms like X(formerly Twitter)and Sina Weibo have become key channels for spreading information online.Accurately predicting information spread,such as users’reposting activities,is essential for applications including content recommendation and analyzing public sentiment.Current advanced models rely on deep representation learning to extract features from various inputs,such as users’social connections and repost history,to forecast reposting behavior.Nonetheless,these models frequently ignore intrinsic confounding factors,which may cause the models to capture spurious relationships,ultimately impacting prediction performance.To address this limitation,we propose a novel Debiased Reposting Prediction model(DRP).Our model mitigates the influence of confounding variables by incorporating intervention operations from causal inference,enabling it to learn the causal associations between features and user reposting behavior.Specifically,we introduce a memory network within DRP to enhance the model’s perception of confounder distributions.This network aggregates and learns confounding information dispersed across different training data batches by optimizing the reconstruction loss.Furthermore,recognizing the challenge of acquiring prior knowledge of causal graphs,which is crucial for causal inference,we develop a causal discovery module within DRP(CD-DRP).This module allows the model to autonomously uncover the causal graph of feature variables by analyzing microblogging data.Experimental results on multiple real-world datasets demonstrate that our proposed method effectively uncovers causal relationships between variables,exhibits strong time efficiency,and outperforms state-of-the-art models in prediction performance(improved by 2.54%)and overfitting reduction(by 7.44%).
文摘This study explores the application of Bayesian econometrics in policy evaluation through theoretical analysis. The research first reviews the theoretical foundations of Bayesian methods, including the concepts of Bayesian inference, prior distributions, and posterior distributions. Through systematic analysis, the study constructs a theoretical framework for applying Bayesian methods in policy evaluation. The research finds that Bayesian methods have multiple theoretical advantages in policy evaluation: Based on parameter uncertainty theory, Bayesian methods can better handle uncertainty in model parameters and provide more comprehensive estimates of policy effects;from the perspective of model selection theory, Bayesian model averaging can reduce model selection bias and enhance the robustness of evaluation results;according to causal inference theory, Bayesian causal inference methods provide new approaches for evaluating policy causal effects. The study also points out the complexities of applying Bayesian methods in policy evaluation, such as the selection of prior information and computational complexity. To address these complexities, the study proposes hybrid methods combining frequentist approaches and suggestions for developing computationally efficient algorithms. The research also discusses theoretical comparisons between Bayesian methods and other policy evaluation techniques, providing directions for future research.
基金supported in part by the Key Scientific Technological Innovation Research Project of the Ministry of Education,the Joint Funds of the National Natural Science Foundation of China(U22B2054)the National Natural Science Foundation of China(62076192,61902298,61573267,61906150,and 62276199)+2 种基金the 111 Project,the Program for Cheung Kong Scholars and Innovative Research Team in University(IRT 15R53)the Science and Technology Innovation Project from the Chinese Ministry of Education,the Key Research and Development Program in Shaanxi Province of China(2019ZDLGY03-06)the China Postdoctoral Fund(2022T150506).
文摘Deep learning relies on learning from extensive data to generate prediction results.This approach may inadvertently capture spurious correlations within the data,leading to models that lack interpretability and robustness.Researchers have developed more profound and stable causal inference methods based on cognitive neuroscience.By replacing the correlation model with a stable and interpretable causal model,it is possible to mitigate the misleading nature of spurious correlations and overcome the limitations of model calculations.In this survey,we provide a comprehensive and structured review of causal inference methods in deep learning.Brain-like inference ideas are discussed from a brain-inspired perspective,and the basic concepts of causal learning are introduced.The article describes the integration of causal inference with traditional deep learning algorithms and illustrates its application to large model tasks as well as specific modalities in deep learning.The current limitations of causal inference and future research directions are discussed.Moreover,the commonly used benchmark datasets and the corresponding download links are summarized.
文摘This study aims to conduct an in-depth analysis of social media data using causal inference methods to explore the underlying mechanisms driving user behavior patterns.By leveraging large-scale social media datasets,this research develops a systematic analytical framework that integrates techniques such as propensity score matching,regression analysis,and regression discontinuity design to identify the causal effects of content characteristics,user attributes,and social network structures on user interactions,including clicks,shares,comments,and likes.The empirical findings indicate that factors such as sentiment,topical relevance,and network centrality have significant causal impacts on user behavior,with notable differences observed among various user groups.This study not only enriches the theoretical understanding of social media data analysis but also provides data-driven decision support and practical guidance for fields such as digital marketing,public opinion management,and digital governance.
