Search Results (486)

In observational causal inference studies, unmeasured confounding remains a critical threat to the validity of effect estimates. While proximal causal inference (PCI) has emerged as a powerful framework for mitigating such bias through proxy variables, existing PCI methods cannot directly handle censored data. This article develops a unified proximal causal inference framework that simultaneously addresses unmeasured confounding and right-censoring challenges, extending the proximal causal inference literature. Our key contributions are twofold: (i) We propose novel identification strategies and develop two distinct estimators for the censored-outcome bridge function and treatment confounding bridge function, resolving the fundamental challenge of unobserved outcomes; (ii) To improve robustness against model misspecification, we construct a robust proximal estimator and establish uniform consistency for all proposed estimators under mild regularity conditions. Through comprehensive simulations, we demonstrate the finite-sample performance of our methods, followed by an empirical application evaluating right heart catheterization effectiveness in critically ill ICU patients. Full article

Exploring the action mechanisms and enhancement pathways of the resilience of agricultural product green supply chains is conducive to strengthening the system’s risk resistance capacity and providing decision support for achieving the “dual carbon” goals. Based on theories such as dynamic capability theory and complex adaptive systems, this paper constructs a resilience framework covering the three stages of “steady-state maintenance–dynamic adjustment–continuous evolution” from both single and multiple perspectives. Combined with 768 units of multi-agent questionnaire data, it adopts Structural Equation Modeling (SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the influencing factors of resilience and reveal the nonlinear mechanisms of resilience formation. Secondly, by integrating configurational analysis with machine learning, it innovatively constructs a resilience level prediction model based on fsQCA-XGBoost. The research findings are as follows: (1) fsQCA identifies a total of four high-resilience pathways, verifying the core proposition of “multiple conjunctural causality” in complex adaptive system theory; (2) compared with single algorithms such as Random Forest, Decision Tree, AdaBoost, ExtraTrees, and XGBoost, the fsQCA-XGBoost prediction method proposed in this paper achieves an optimization of 66% and over 150% in recall rate and positive sample identification, respectively. It reduces false negative risk omission by 50% and improves the ability to capture high-risk samples by three times, which verifies the feasibility and applicability of the fsQCA-XGBoost prediction method in the field of resilience prediction for agricultural product green supply chains. This research provides a risk prevention and control paradigm with both theoretical explanatory power and practical operability for agricultural product green supply chains, and promotes collaborative realization of the “carbon reduction–supply stability–efficiency improvement” goals, transforming them from policy vision to operational reality. Full article

This study aimed to develop a Causal Loop Diagram (CLD) to visualise how urban environment factors impact dementia and cognitive decline, and potential causal mechanisms. In Group Model Building workshops with 12 researchers, a CLD was created to identify factors contributing to cognitive decline, and the dynamic interrelationships between these factors. The factors were classified in nine main themes: urban design, social environment, travel behaviours, urban design by-products, lifestyle, mental health conditions, disease/physiology, brain physiology, and cognitive decline outcomes. Five selected feedback loops illustrated some dynamics in the system. The workshops helped develop a shared language and understanding of different perspectives from an interdisciplinary team. The CLD creation was part of a comprehensive modelling approach based on experts’ knowledge which informed other research outputs such as an evidence gap map and an umbrella review, helped the identification of environmental variables for future studies and analyses, and helped to identify future possible systems-based interventions to prevent cognitive decline. The study highlights the utility of CLDs and Group Model Building workshops in interdisciplinary research projects investigating complex systems. Full article

Background: Oral candidiasis (OC) and vulvovaginal candidiasis (VVC) are infections caused by species belonging to the genus Candida. In Chile, epidemiological studies on OC/VVC are scarce, leading to an overestimation of the prevalence of C. albicans. Additionally, awareness of the prevalence of species phenotypically and genotypically similar to C. albicans is lacking. The clinical impact of non-albicans species in cases of OC/VVC is also often underestimated. This study aims to determine the distribution of Candida species, their phenotypic and molecular characteristics, and their antifungal susceptibility patterns in incidents of oral and vulvovaginal candidiasis in Chile. Methods: A descriptive analysis was conducted on 101 isolates of Candida spp. obtained from OC/VVC cases. The identification of Candida species was performed using both phenotypic and molecular techniques. Antifungal susceptibility testing was carried out using the Sensititre YeastOne system. Results: Among the analyzed isolates, 89.1% were identified as C. albicans, while 10.9% were categorized as non-albicans species, including C. dubliniensis, C. glabrata sensu stricto, C. bracarensis, C. tropicalis, C. lusitaniae, and C. parapsilosis sensu stricto. The susceptibility pattern was predominantly susceptible, with only 10.9% of the total strains demonstrating resistance, and low antifungal activity in vitro was observed for Fluconazole, Voriconazole, and Posaconazole. Conclusions: The most prevalent species causing OC/VVC in Chile is C. albicans. This study also presents the first report of C. lusitaniae as a causal agent of VVC in the country. The identification of azole-resistant strains emphasizes the critical role of laboratory diagnosis in VVC cases, thereby preventing potential treatment failures. No resistance was observed in the strains associated with OC. Full article

Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of global morbidity and mortality, prompting significant interest in individualized prevention and treatment strategies. This review synthesizes recent advances in genomic and precision medicine approaches relevant to ASCVD, with a focus on genetic risk scores, lipid metabolism genes, and emerging gene editing techniques. A structured literature search was conducted across PubMed, Scopus, and Web of Science databases to identify key publications from the last decade addressing genomic mechanisms, therapeutic targets, and computational tools in ASCVD. Notable findings include the identification of causal genetic variants such as PCSK9 and LDLR, the development of polygenic risk scores for early prediction, and the use of deep learning algorithms for integrative multi-omics analysis. In addition, we highlight current and future therapeutic applications including PCSK9 inhibitors, RNA-based therapies, and CRISPR-based genome editing. Collectively, these advances underscore the promise of precision medicine in tailoring ASCVD prevention and treatment to individual genetic and molecular profiles. Full article

Colorectal cancer (CRC) is one of the most prevalent and lethal oncological diseases worldwide, with a concerning rise in incidence, particularly in developing countries. Recent advances in genetic sequencing have revealed that the gut microbiome plays a crucial role in CRC development. Mechanisms such as chronic inflammation, metabolic alterations, and oncogenic pathways have demonstrated that dysbiosis, a disruption of the gut microbiome, is linked to CRC. Associations have been found between tumor progression, treatment resistance, and pathogenic microbes such as Fusobacterium nucleatum and Escherichia coli. A promising approach for CRC prevention and treatment is microbiome manipulation through interventions such as probiotics, prebiotics, fecal microbiota transplantation, and selective antibiotics. This article explores how gut microbiome alterations influence CRC pathogenesis and examines microbiome modulation strategies currently used as adjuncts to traditional treatments. Advances in artificial intelligence, single-cell and spatial transcriptomics, and large-scale initiatives such as the ONCOBIOME Project are paving the way for the identification of microbiome-derived biomarkers for early CRC detection and personalized treatment. Despite promising progress, challenges such as interindividual variability, causal inference, and regulatory hurdles must be addressed. Future integration of microbiome analysis into multi-omics frameworks holds great potential to revolutionize precision oncology in CRC management. Full article

The usual inputs for a causal identification task are a graph representing qualitative causal hypotheses and a joint probability distribution for some of the causal model’s variables when they are observed rather than intervened on. Alternatively, the available probabilities sometimes come from a combination of passive observations and controlled experiments. It also makes sense, however, to consider causal identification with data collected via schemes more generic than (perfect) passive observation or perfect controlled experiments. For example, observation procedures may be noisy, may disturb the variables, or may yield only coarse-grained specification of the variables’ values. In this work, we investigate identification of causal quantities when the probabilities available for inference are the probabilities of outcomes of these more generic schemes. Using process theories (aka symmetric monoidal categories), we formulate graphical causal models as second-order processes that respond to such data collection instruments. We pose the causal identification problem relative to arbitrary sets of available instruments. Perfect passive observation instruments—those that produce the usual observational probabilities used in causal inference—satisfy an abstract process-theoretic property called marginal informational completeness. This property also holds for other (sets of) instruments. The main finding is that in the case of Markovian models, as long as the available instruments satisfy this property, the probabilities they produce suffice for identification of interventional quantities, just as those produced by perfect passive observations do. This finding sharpens the distinction between the Markovianity of a causal model and that of a probability distribution, suggesting a more extensive line of investigation of causal inference within a process-theoretic framework. Full article

Video-based person re-identification (re-identification) remains underused in real-world deployments, despite impressive benchmark performance. Most existing models rely on superficial correlations—such as clothing, background, or lighting—that fail to generalize across domains, viewpoints, and temporal variations. This study examines the emerging role of causal reasoning as a principled alternative to traditional correlation-based approaches in video-based re-identification. We provide a structured and critical analysis of methods that leverage structural causal models (SCMs), interventions, and counterfactual reasoning to isolate identity-specific features from confounding factors. This study is organized around a novel taxonomy of causal re-identification methods, spanning generative disentanglement, domain-invariant modeling, and causal transformers. We review current evaluation metrics and introduce causal-specific robustness measures. In addition, we assess the practical challenges—scalability, fairness, interpretability, and privacy—that must be addressed for real-world adoption. Finally, we identify open problems and outline future research directions that integrate causal modeling with efficient architectures and self-supervised learning. This study aims to establish a coherent foundation for causal video-based person re-identification and catalyze the next phase of research in this rapidly evolving domain. Full article

Event Causality Identification (ECI) is a crucial task in Information Extraction (IE). However, information about events described in documents is often distributed across sentences, which makes it difficult for existing studies to capture long-distance causal relations between events. To address these issues, this paper proposes Iterative Event Causal Identification (IECI), a pipelined framework for event causality identification that integrates two modules. The first module introduces Prompt-Based Event Detection (PRED), which integrates semantic role awareness with prompt templates to provide foundational input for the next module. The second module proposes the Semantic-Role Guided Causal Inference Graph (SRCIG), which identifies causal relations between events by constructing a causal graph and applying a dynamic threshold adjustment mechanism during the iterative process. Our experiments show that PRED and IECI consistently outperform the state-of-the-art baseline model. Specifically, on the EventStoryLine dataset, they achieve F1 improvements of 3.7–9.8% and 4.2–18.8%, respectively, while on MAVEN-ERE the gains are 4.2–10.3% and 1.0–40.3%. This demonstrates the effectiveness and robustness of the proposed framework in both event detection and event causality identification. Full article

As air transportation demand continues to rise, congestion in air route networks has seriously compromised the safe and efficient operation of air traffic. Few studies have examined the spatiotemporal characteristics of congestion propagation under different time lag conditions. To address this gap, this study proposes a cross-segment congestion-propagation causal time-lag analysis framework. First, to account for the interdependency across segments in air route networks, we construct a point–line congestion state assessment model and introduce the FCM-WBO algorithm for precise congestion state identification. Next, the Multi-Channel Attention DSNG-BiLSTM model is designed to estimate the causal weights of congestion propagation between segments. Finally, based on these causal weights, two indicators—CPP and CPF—are derived to analyze the spatiotemporal characteristics of congestion propagation under various time lag levels. The results indicate that our method achieves over 90% accuracy in estimating causal weights. Moreover, the propagation features differ significantly in their spatiotemporal distributions under different time lags. Spatially, congestion sources tend to spread as time lag increases. We also identify segments that are likely to become overloaded, which serve as the primary receivers of congestion. Temporally, analysis of time-lag features reveals that because of higher traffic flow during peak periods, congestion propagates 36.92% more slowly than during the early-morning hours. By analyzing congestion propagation at multiple time lags, controllers can identify potential congestion sources in advance. They can then implement targeted interventions during critical periods, thereby alleviating congestion in real time and improving route-network efficiency and safety. Full article

Trespassing on railway tracks is a growing problem in rail transport, with multiple causal factors, including increasing urbanisation, high-frequency rail traffic, higher volumes of traffic, etc. The predominant factor is human behaviour (lack of knowledge about trespassing, poor decision-making by road users and others). This research aims to analyse the available data to determine the frequency, patterns, and factors contributing to trespassing on railway tracks and to identify potential locations with the highest recorded trespassing. This is achieved by conducting a case study using data from various sources on trespassing from 2001 to 2023 on the Italian railway network. The methodology of this study consists of data collection on trespassing, data cleaning, and three-step analysis (description of variables used, and application of R programming language for descriptive statistics, correlation, and association analysis). The outcome of this study is the description of the data collecting process of trespassing on the Italian railway network, the identification of temporal factors, e.g., month, day, and hour of trespassing, and spatial factors, e.g., location and railway line where trespassing occurs most frequently, and a list of current and planned prevention measures on the Italian railway network. In the future, trespassing locations can be analysed according to the topology of risk. Full article

Lemon, widely used in food, medicine, cosmetics, and other industries, has considerable value as a commodity and horticultural product. Previous research has shown that the fungus Diaporthe citri infects several citrus species, including mandarin, lemon, sweet orange, pomelo, and grapefruit, in China. Although D. citri has been reported to cause melanose disease in lemons in China, key pathological evidence, such as Koch’s postulates fulfillment on lemon fruits and detailed morphological characterization, is still lacking. In May 2018, fruits, leaves, and twigs were observed to be infected with melanose disease in lemon orchards in Chongqing municipality in China. The symptoms appeared as small black discrete spots on the surface of fruits, leaves, and twigs without obvious prominent and convex pustules. D. citri was isolated consistently from symptomatic organs and identified provisionally based on the morphological characteristics. The identification was confirmed using sequencing and multigene phylogenetic analysis of ITS, TUB, TEF, HIS, and CAL regions. Pathogenicity tests were performed using a conidium suspension, and melanose symptoms similar to those observed in the field were reproduced. To our knowledge, this study provides the first comprehensive evidence for D. citri as a causal agent of melanose disease in lemons in China, including morphological characterization and pathogenicity assays on lemon fruits. This report broadens the spectrum of hosts of D. citri in China and provides useful information for the management of melanose in lemons. Full article

To make predictions, one can use machine learning and/or knowledge-based approaches. Knowledge-based approaches focus on developing systems with reasoning capabilities to solve application problems. Traditionally, statistical techniques have been used, while more recently, machine learning techniques have been used to make predictions. Both types of techniques are based almost exclusively on the analysis of historical data. This paper proposes a model that combines knowledge engineering and intelligent data analysis, leveraging the causal relationship between a past event and its known consequences. By determining the similarity between a current analogous situation and the past event, the model infers what the consequences of the current situation might be. The main contribution is the combination of various knowledge engineering techniques to improve the prediction outcomes for certain events. The present approach not only relies on analysing historical data but also integrates smart data utilization, the identification of the most similar past event, and the prediction or definition of cause–effect rules based on causal inference. One use case is presented: predicting the percentage of students who are promoted to the next grade with all subjects passed over the four years of middle school. Applying statistical regression techniques, a predicted value of 68.67% was obtained. Applying the proposed model, a value of 62.85% was obtained. The actual value published by the Spanish Department of Education for the 2021–2022 school year was 63.95%. The prediction using statistical techniques deviated 7.3% from the actual value. The proposed method deviated only 1.7% from the actual value. The proposed method improved the prediction compared to the value obtained using statistical techniques. Full article

The traditional chemical safety management method mainly relies on manual inspection and empirical judgment, which is incompetent in the face of the increasingly complex production environment and colossal data volume, and there is an urgent need to apply efficient modern emerging technologies to strengthen the safety management of chemical production sites. Therefore, this dissertation researches chemical safety risk factor identification and analysis predicated on improved LDA topic model and Bayesian network. Thirty-three main risk factors are obtained by constructing the LDA topic model, text mining, and thematic analysis of chemical safety accident cases and combining them with the socio-technical system accident model. The correlation and causal relationship between risk factors were revealed based on association rule mining and Bayesian network analysis. Sensitivity and critical causal path analyses were utilized to indicate the possible paths and vital aspects of accident development. The results show that the text mining LDA topic model proposed in the dissertation performs well in analyzing accident reports and can effectively solve the problems of insufficient analyzing ability and high subjectivity of traditional methods. The research method of the thesis can efficiently extract the keywords of accident reports and reveal the correlation and causality between risk factors. Full article

Background/Objectives: The prevalence of dementia among South Asians across India is high among those who are 65 years and older, yet little is known about genetic risk factors for dementia in this population. Methods: Using whole-genome sequence data from 2680 participants from the Diagnostic Assessment of Dementia for the Longitudinal Aging Study of India (LASI-DAD), we performed a gene-based analysis on the missense/loss-of-function (LoF) and brain-specific promoter/enhancer variants of 84 genes, previously associated with AD in European Ancestry (EA). These analyses were performed separately, both with and without incorporating additional annotation weights (e.g., deleteriousness, conservation scores), using the variant-Set Test for Association using Annotation infoRmation (STAAR). We investigated associations with the Hindi Mental State Examination (HMSE) score and factor scores for general cognitive function and five cognitive domains. Results: In the missense/LoF analysis, without annotation weights and controlling for age, sex, state/territory, and genetic ancestry, three genes were associated with at least one measure of cognitive function (FDR q < 0.1). APOE was associated with four measures of cognitive function, PICALM was associated with HMSE score, and TSPOAP1 was associated with executive function. The most strongly associated variants in each gene were rs429358 (APOE ε4), rs779406084 (PICALM), and rs9913145 (TSPOAP1). Rs779406084 is a rare missense mutation that is enriched in LASI-DAD compared to EA (minor allele frequency = 0.075% vs. 0.0015%). Conclusions: Missense/LoF variants in some genes previously associated with AD in EA are associated with measures of cognitive function in South Asians from India. Analyzing genome sequence data allows the identification of potential novel causal variants enriched in South Asians. Full article