Causal Relationship Research Articles

ALLSTAR: Inference of ReliAble CausaL RuLes between Somatic MuTAtions and CanceR Phenotypes.

Recent advances in DNA sequencing technologies have allowed the detailed characterization of genomes in large cohorts of tumors, highlighting their extreme heterogeneity, with no two tumors sharing the same complement of somatic mutations. Such heterogeneity hinders our ability to identify somatic mutations important for the disease, including mutations that determine clinically relevant phenotypes (e.g., cancer subtypes). Several tools have been developed to identify somatic mutations related to cancer phenotypes. However, such tools identify correlations between somatic mutations and cancer phenotypes, with no guarantee of highlighting causal relations. We describe ALLSTAR, a novel tool to infer reliable causal relations between somatic mutations and cancer phenotypes. ALLSTAR identifies reliable causal rules highlighting combinations of somatic mutations with the highest impact in terms of average effect on the phenotype. While we prove that the underlying computational problem is NP-hard, we develop a branch-and-bound approach that employs protein-protein interaction networks and novel bounds for pruning the search space, while properly correcting for multiple hypothesis testing. Our extensive experimental evaluation on synthetic data shows that our tool is able to identify reliable causal relations in large cancer cohorts. Moreover, the reliable causal rules identified by our tool in cancer data show that our approach identifies several somatic mutations known to be relevant for cancer phenotypes as well as novel biologically meaningful relations. Code, data, and scripts to reproduce the experiments available at https://github.com/VandinLab/ALLSTAR. Supplementary data are available at Bioinformatics online.

Feeble, fallen, and forgetting: association of cognitive impairment and falls on outcomes of major intra-abdominal surgeries in older adults

BackgroundBoth cognitive impairment/dementia (CID) and falls occur more commonly in older adults than in younger patients. This study aimed to analyze the association of a history of CID or falls with the postoperative outcomes of older adults undergoing major intra-abdominal surgeries on a national level. MethodsWe retrospectively analyzed the American College of Surgeons–National Surgical Quality Improvement Program 2022 Participant Use Data File. Our primary outcome was postoperative mortality. Statistical analysis was performed using the Chi-square test and multivariate regression analysis. ResultsOn multivariable regression analyses, a history of both CID (odds ratio [OR] = 1.9; CI: 1.5–2.5; P < .01) and a fall (OR = 1.8; CI: 1.4–2.3; P < .01) were independently associated with higher adjusted odds of mortality. History of CID or falls was also a predictor of overall complications, major complications, and discharge to a care facility. ConclusionA history of CID or falls in older adults before major intra-abdominal surgeries was associated with a high risk of postoperative mortality and morbidity. Further studies are required to establish the causal relation of these factors and the steps to mitigate the risk of associated adverse outcomes.

On The Possible Redundancy of the Third Noble Truth

Abstract: We raise and attempt to resolve the charge that the third noble truth of the core Buddhist teachings is redundant. If the second noble truth asserts a causal relation between craving and suffering, and if James Woodward’s interventionist account of causation is correct, we argue that these premises are sufficient to entail the difference-making described by the third noble truth. Thus, the third noble truth would be superfluous insofar as it merely makes explicit what must be the case, according to a plausible theory of causation, if the second noble truth is true. Having raised the possibility that the third noble truth is redundant, we consider five possible responses to the charge, the last of which we endorse. Even if the redundancy charge is found not to stick, we believe an investigation into why this is so highlights features of the third noble truth that might be suppressed under standard glosses of its meaning.

Adolescent Birth Rate: Between Women Participation in Parliament or Women Population with Secondary Education

This study is constructed to find causal relation between women's participation in parliament (X1) and the women population with secondary education (X2) toward adolescence birth rate in the world. The data was taken from UNDP Gender Inequality Index. This research used a quantitative method with an explanatory design in which the researchers try to see how two independent variables affect the dependent variable using multilinear regression. The finding shows that X1 generates positive causal inference while X2 creates negative causal reference toward the dependent variable with 0.009547 and -0.730165. It means that based on the hypothesis testing using multiple linear regression, it is revealed that the first hypothesis is rejected, and the competing hypothesis is accepted. By means that the involvement of women in parliament is not significantly affected in reducing the adolescent birth rate yet the higher population in secondary education is proven statistically significant in causing a lower adolescent birth rate in the world.

High Resilient Asymmetry and Anomaly Detection Based on Data Causality

In the tunnel construction practice, multiple buildings’ tilt rate data are collected. In this study, data causality is defined to reflect the causal relation between the input and output of the building tilt rate detection data. Upon defining and calculating the data causality, a new high resilient causality detection (HiReCau) method is proposed for abnormal building tilt rate detection. A numerical case and another practical case are studied for validation purposes. The case study results show that the proposed HiReCau method can accurately detect high-causality data and low-causality data among the building tilt rate detection data and produces superior results compared with the direct adoption of a machine learning approach. Furthermore, the resilience of HiReCau is validated by investigations testing varied levels of additional low-causality data in the training dataset. Presently, HiReCau is limited to handling problems with a single output. Furthermore, only the back-propagation neural network (BPNN) is tested as the baseline model and there is also room to further expand the data size. The proposed approach is versatile and able to be adjusted to handle fault diagnosis and safety assessment problems in varied theoretical and engineering backgrounds.

Asymmetric Analysis of Causal Relations in the Informality–Globalisation Nexus in Africa

This study examines the causal relationship between informality and globalisation in 30 African countries. It deviates from traditional research by adopting a bi-directional framework to address reverse causality. By applying the DH causality method in both linear and nonlinear frameworks, this research challenges the assumption of a linear relationship and finds that the causal structure is better explained within a nonlinear asymmetric context. This paper provides recommendations based on the identified causal relationships. For countries in which globalisation leads to informality, such as Angola, Congo, Guinea, Gambia, Mozambique, Sierra Leone, Tunisia, Tanzania, Uganda, Zambia, and Zimbabwe, the paper suggests policy measures to integrate the informal sector into the formal economy. These measures include designing programmes to facilitate transition, implementing skill development initiatives, and establishing support mechanisms for entrepreneurship and small businesses. Additionally, this paper advises the development of social safety nets, improved market access, effective monitoring and regulation mechanisms, education on the benefits of globalisation, and international cooperation. For countries experiencing positive shocks from informality to globalisation, this paper recommends targeted support programs for entrepreneurship, initiatives to formalize the sector, the enhancement of market access, and skill development tailored to the needs of the informal sector. These policy recommendations aim to capitalize on the positive shocks in informality by fostering entrepreneurship, formalization, market access, and skill development. In the case of negative shocks in globalisation leading to positive shocks in informality, the paper suggests implementing resilience-building policies for the informal sector during economic downturns, establishing social safety nets, and adopting flexible labour policies.

Integrating Structural Causal Model Ontologies with LIME for Fair Machine Learning Explanations in Educational Admissions

This study employed knowledge discovery in databases (KDD) to extract and discover knowledge from the Benue State Polytechnic (Benpoly) admission database and used a structural causal model (SCM) ontological framework to represent the admission process in the Nigerian polytechnic education system. The SCM ontology identified important causal relations in features needed to model the admission process and was validated using the conditional independence test (CIT) criteria. The SCM ontology was further employed to identify and constrain input features causing bias in the local interpretable model-agnostic explanations (LIME) framework applied to machine learning (ML) black-box predictions. The ablation process produced more stable LIME explanations devoid of fairness bias compared to LIME without ablation, with higher prediction accuracy (91% vs. 89%) and F1 scores (95% vs. 94%). The study also compared the performance of different ML models, including Gaussian Naïve Bayes, Decision Trees, and Logistic Regression, before and after ablation. The limitation is that the SCM ontology is qualitative and context-specific, so the fair-LIME framework can only be extrapolated to similar contexts. Future work could compare other explanation frameworks like Shapley on the same dataset. Overall, this study demonstrates a novel approach to enforcing fairness in ML explanations by integrating qualitative SCM ontologies with quantitative ML/LIME methods.

Effect of Energy Utilization on Pakistan’s Economic Development: A Time Series Analysis

The present research has been conducted to study the causal relation among GDP, electricity utilization, exports, real capital and labor force for Pakistan. Time dependent data for the mentioned parameters have been used for the time period of 1980 to 2022. Inter-relations among the above-mentioned variables have been studied in this work by the method of cointegration using bounds test. The results illustrate that there is an existence of long run relation among the parameters where GDP has been taken into consideration as the dependent variable. Granger causation analysis has also been performed for the variables. Results show that Granger causality between GDP and electricity utilization runs in both directions. Moreover, the study discloses that Electricity utilization granger cause exports and per capita real capital. Exports granger cause per capita real capital. Per capita real capital granger cause GDP. Labor force granger cause GDP and exports. The long run relation equation of GDP, Electricity utilization, exports, real capital and labor force has also been examined for parameter stability. The parameters are found to be stable with the significance level of 5%. The research also suggests some significant strategy recommendations.

A graph propagation model with rich event structures for joint event relation extraction

The task of event relation extraction (ERE) aims to organize multiple events and their relations as a directed graph. However, existing ERE methods exhibit two limitations: (1) Events in a document are typically expressed with merely a trigger word or phrase neglecting the rich semantic structure of event arguments and roles. (2) Various event relations such as coreference, temporal, causal, and subevent relations are correlated with each other, while little work has explored the benefits of extracting all relations in a joint manner. In this work, we investigate an event-relation graph propagation network and a novel document-level semantically-enriched event representation for joint ERE. First, we address the lack of event argument annotations by extracting cross-sentence implicit arguments based on the AMR graph. Then triggers and argument roles are aggregated with a structure-aware encoder. Second, based on the rich event information, event relation interactions are incorporated with a subgraph propagation and aggregation mechanism. During training, we further develop a novel triadic contrastive loss to capture high-order event pair relationships. We conduct experiments based on the public MAVEN-ERE benchmark and the results show that our model achieves 60.7%, 37.4%, 32.9% F1-scores on temporal, causal and subevent relations, and 86.1% MUC-score on coreferences, outperforming the current joint models by a large margin. Further in-depth analysis shows the effectiveness of our model in capturing event-event dependencies in document context. The proposed model can be used for event graph construction and storyline understanding.

Longitudinal associations among basic psychological need satisfaction at school, self-esteem, and suicidal ideation from middle childhood to early adolescence: Disentangling between‑ and within‑person associations.

The current study aimed to examine the longitudinal associations among basic psychological need satisfaction at school (BPNSS), self-esteem, and suicidal ideation (SI), including whether self-esteem functioned as a mediator of the relations between BPNSS and SI at the within-person level after disentangling between- and within-person associations encompassing middle childhood to early adolescence. A total of 650 Chinese students (53.54% boys, Mage = 9.95, SD = 0.75 at Time 1) completed measures on four occasions across 1.5 years, using 6-month intervals. Random intercept cross-lagged panel models were applied to disaggregate between- and within-person effects, thus providing greater confidence in elucidating the causal relations among study variables. The results showed that at the within-person level: (a) BPNSS negatively predicted SI; (b) BPNSS positively predicted self-esteem; (c) Self-esteem negatively predicted SI; and (d) BPNSS indirectly predicted SI via self-esteem. These findings advanced the literature by demonstrating longitudinal associations among BPNSS, self-esteem, and SI at the within-person level, and highlighting the significance of distinguishing between- and within-person effects in developing prevention and intervention programs aimed at reducing SI over time from middle childhood to early adolescence.

Event Causality Identification via Competitive-Cooperative Cognition Networks

Identifying the causal relations between events is an important task in natural language processing (NLP). However, existing methods mainly leverage human empirical information about causality from limited labeled corpus or pseudo-data, which leads to an insufficient understanding of the causal mechanism. Inspired by the dual process theory (DPT) in cognitive science, we propose Competitive-Cooperative Cognition Networks (C3Net), including an intuitive causal reasoner, a logical causal reasoner, and a competitive selection mechanism, which directly learn and imitate the framework of the causal identification process in human brains. On the one hand, our model can inject external causal knowledge into the intuitive causal reasoner to improve the performance of intuitive reasoning. On the other hand, it can make explicit logical reasoning in a three-stage paradigm, mimicking human reasoning behavior: association→review→inference. Furthermore, we devise a novel competitive selection mechanism based on private evidence and historical patterns of each reasoner to determine which reasoner’s inference is more reliable. Experiments demonstrate that our approach can achieve better performance than previous baseline methods.

A dynamic foot model for predictive simulations of human gait reveals causal relations between foot structure and whole-body mechanics.

The unique structure of the human foot is seen as a crucial adaptation for bipedalism. The foot's arched shape enables stiffening the foot to withstand high loads when pushing off, without compromising foot flexibility. Experimental studies demonstrated that manipulating foot stiffness has considerable effects on gait. In clinical practice, altered foot structure is associated with pathological gait. Yet, experimentally manipulating individual foot properties (e.g. arch height or tendon and ligament stiffness) is hard and therefore our understanding of how foot structure influences gait mechanics is still limited. Predictive simulations are a powerful tool to explore causal relationships between musculoskeletal properties and whole-body gait. However, musculoskeletal models used in three-dimensional predictive simulations assume a rigid foot arch, limiting their use for studying how foot structure influences three-dimensional gait mechanics. Here, we developed a four-segment foot model with a longitudinal arch for use in predictive simulations. We identified three properties of the ankle-foot complex that are important to capture ankle and knee kinematics, soleus activation, and ankle power of healthy adults: (1) compliant Achilles tendon, (2) stiff heel pad, (3) the ability to stiffen the foot. The latter requires sufficient arch height and contributions of plantar fascia, and intrinsic and extrinsic foot muscles. A reduced ability to stiffen the foot results in walking patterns with reduced push-off power. Simulations based on our model also captured the effects of walking with anaesthetised intrinsic foot muscles or an insole limiting arch compression. The ability to reproduce these different experiments indicates that our foot model captures the main mechanical properties of the foot. The presented four-segment foot model is a potentially powerful tool to study the relationship between foot properties and gait mechanics and energetics in health and disease.

Bayesian inference of causal relations between dynamical systems

From ancient philosophers to modern economists, biologists, and other researchers, there has been a continuous effort to unveil causal relations. The most formidable challenge lies in deducing the nature of the causal relationship: whether it is unidirectional, bidirectional, or merely apparent — implied by an unobserved common cause.While modern technology equips us with tools to collect data from intricate systems such as the planet’s ecosystem or the human brain, comprehending their functioning requires the identification and differentiation of causal relationships among the components, often without external interventions.In this context, we introduce a novel method capable of distinguishing and assigning probabilities to the presence of all potential basic causal relations between two or more time series within dynamical systems. The efficacy of this method is verified using synthetic datasets and applied to EEG (electroencephalographic) data recorded from epileptic patients.Given the universal applicability of our method, it holds promise for diverse scientific fields.

Fair Feature Selection: A Causal Perspective

Fair feature selection for classification decision tasks has recently garnered significant attention from researchers. However, existing fair feature selection algorithms fall short of providing a full explanation of the causal relationship between features and sensitive attributes, potentially impacting the accuracy of fair feature identification. To address this issue, we propose a fair causal feature selection algorithm, called FairCFS . Specifically, FairCFS constructs a localized causal graph that identifies the Markov blankets of class and sensitive variables, to block the transmission of sensitive information for selecting fair causal features. Extensive experiments on seven public real-world datasets validate that FairCFS has accuracy comparable to eight state-of-the-art feature selection algorithms while presenting more superior fairness.

Causal relationship between gut microbiota and functional outcomes after ischemic stroke: A comprehensive Mendelian randomization study

AimsTo investigate the association of the genetic predisposition of specific gut microbiotas with the clinical outcome of ischemic stroke. MethodsWe leveraged publicly available genome-wide association study (GWAS) data to perform Mendelian randomization (MR) analysis. The gut microbiota-related GWAS data from 18,340 individuals from the international consortium MiBioGen was used. The summary data for functional outcomes after ischemic stroke was obtained from the Genetics of Ischemic Stroke Functional Outcome (GISCOME) network meta-analysis. The primary outcomes were judged by the modified Rankin Scale (mRS). The principal analyses were conducted using the inverse-variance weighted (IVW) MR method. The Cochran's Q test, weighted median, MR-Egger regression, leave-one-SNP-out analysis, MR-Pleiotropy Residual Sum, and Outlier methods were adopted as sensitivity analyses. Furthermore, we performed bi-directional MR analysis and the MR Steiger directionality test to examine the direction of the causal relations. ResultsThe results demonstrated that the genetic predisposition of genus Lactococcus, genus Ruminococcaceae NK4A214 group, family Peptostreptococcaceae, and genus Odoribacter was positively associated with favorable functional outcome after ischemic stroke. Genus Collinsella, genus Ruminococcaceae UCG005, genus Akkermansia, genus Eubacterium oxidoreducens group, and family Verrucomicrobiaceae were identified to be associated with worse functional outcomes after ischemic stroke. Our results showed no evidence of heterogeneity, directional pleiotropic effects, or collider bias, and the sensitivity of our analysis was acceptable. ConclusionThe genetic predisposition of different gut microbiotas was associated with the clinical outcome of ischemic stroke. Microbiota adjustment was a promising method to improve the clinical outcome of ischemic stroke.

Supply chain resilience and international manufacturing strategy: a case study from the semiconductor industry

PurposeThis paper studies the interrelations between the concepts of supply chain resilience and international manufacturing strategy. On the basis of an in-depth case study of a company in the semiconductor industry, the paper seeks to integrate the concept of resilience into international manufacturing strategy.Design/methodology/approachIn an explorative qualitative single case study of a semiconductor manufacturer, a systems thinking model is developed from expert interviews and literature research that displays the interrelations of constituent constructs of supply chain resilience and international manufacturing strategy.FindingsForecast accuracy and organizational inertia are identified as barriers to resilience, whereas information technology (IT) capabilities and vertical integration are identified as major levers. Causal relations between constructs are identified, and a concrete suggestion for theory refinement of the manufacturing strategy framework of Miltenburg (2009) is provided.Originality/valuePrior literature on international manufacturing networks (IMNs) has not sufficiently taken into account the importance of resilience in the formulation of international manufacturing strategies. This paper aids in the integration of this increasingly important concept, in a critical industry that has recently been subject to major disruptions.

Integrative Analysis by Mendelian Randomization and Large-Scale Single-Cell Transcriptomics Reveals Causal Links between B Cell Subtypes and Diabetic Kidney Disease

Introduction: The increasing incidence of diabetic kidney disease (DKD) and the challenges in its management highlight the necessity for a deeper understanding of its pathogenesis. While recent studies have underscored the substantial impact of circulating immunity on the development of diabetic microvascular complications such as retinopathy and neuropathy, research on circulating immunity in DKD remains limited. Methods: This study utilized Mendelian randomization analysis to explore the potential independent causal relationships between circulating immune cells and DKD pathogenesis. Additionally, a combination of single-cell disease relevance score (scDRS) and immune cell infiltration analysis was employed to map the circulating immunity landscape in DKD patients. Results: Ten immune traits, including 5 of B cells, 2 of T cells, 2 of granulocytes, and one of monocytes, were defined to be associated with the pathogenesis of DKD. Notably, IgD-CD27-B cell Absolute Count (IVW: OR, 1.102 [1.023–1.189], p = 0.011) and IgD-CD24-B cell Absolute Count (IVW: OR, 1.106 [1.030–1.188], p = 0.005) were associated with promoting DKD pathogenesis, while CD24+CD27+B cell %B cell (IVW: OR, 0.943 [0.898–0.989], p = 0.016) demonstrated a protective effect against DKD onset. The presence of B cell-activating factor receptor (BAFF-R) on CD20−CD38−B cell (IVW: OR, 0.946 [0.904–0.989], p = 0.015) and BAFF-R on IgD-CD38+B cell (IVW: OR, 0.902 [0.834–0.975], p = 0.009) also indicated a potential role in preventing DKD. scDRS analysis revealed that two main subsets of B cells, naïve B and memory B cells, had a higher proportion of DKD-related cells or a higher scDRS score of DKD phenotype, indicating their strong association with DKD. Furthermore, immune infiltrate deconvolution analysis showed a notable decrease in the circulating memory B cells and class-switched memory B cells in DKD patients compared to those of DM patients without DKD. Conclusion: Our study revealed the causal relations between circulating immunity and DKD susceptibility, particularly highlighted the potential roles of B cell subtypes in DKD development. Further studies addressing the related mechanisms would broaden the current understanding of DKD pathogenesis.

From comparative to causal relations: the case of siccome ‘because’ in the history of Italian

Abstract This article investigates how adverbial causal clauses come into being by tracking the diachronic development of the lexical item siccome ‘because’ from Old to Contemporary Italian. We show that adverbial causal clauses introduced by siccome in Contemporary Italian originate from comparative-similative clauses. By describing the steps of this diachronic change, we demonstrate that in specific contexts the comparative-similative marker has paved the way for a comparison between two events entailing a causal relation. The change is formalized by adopting a free relative clause analysis (Cinque 2020a. On the double-headed analysis of “headless” relative clauses. In Ludovico Franco &amp; Paolo Lorusso (eds.), Linguistic variation: Structure and interpretation, 169–196. Berlin: Mouton de Gruyter) to comparative and causal clauses.

Enhancing Workplace Productivity: The Nexus of Organizational Culture and Policies in Public Institutions

Aim – This study aims to explore the intricate interplay between organizational culture, policies, and productivity within a major public institution in Ghana. The primary purpose is to investigate the relationships between organizational policies, characteristics of organizational culture, and non-financial indicators of organizational productivity. Design/methodology/approach – This study utilizes a descriptive survey design, using a sample of 119 respondents randomly selected from a population of 1489 employees. Advanced statistical techniques, including Pearson Correlation and Multiple Linear Regression, are employed to identify relationships and test hypotheses. Findings – The results indicate positive and statistically significant relation-ships between organizational policies, cultural characteristics, and productivi-ty. Multiple linear regression analysis supported the hypothesis, demonstrat-ing a substantial influence of these factors on productivity. Notably, a moder-ate to strong positive correlation was found, highlighting the critical role of organizational culture in fostering improved performance. Limitations – Reliance on self-reported data raises concerns about social de-sirability bias. Additionally, the descriptive survey design allows for the identification of correlations, but causal relationships between variables can-not be established. Practical implications – This research highlights the importance of strategic policies and a supportive culture for public sector productivity. Clear policy implementation, leadership fostering a positive work environment, and en-gagement in policymaking likely enhance performance, particularly within diverse workforces. However, future research should explore causal relation-ships between these factors and productivity for deeper understanding and continuous optimization. Originality/value – This study investigates the combined effects of organiza-tional culture, policy clarity, and employee participation on productivity in a Ghanaian public institution. It contributes to the literature by confirming the positive influence of a supportive culture and well-defined policies, while emphasizing the importance of employee involvement in policy develop-ment. These findings offer practical insights for leaders seeking to enhance productivity in similar contexts.

Examining subjective leisure experiences and well-being amongst university students during COVID-19

ABSTRACT There are many claims in scholarship about the associations between leisure and well-being. However, empirical research is needed to disentangle various conceptualizations and operationalizations of the concepts. This study tested nuanced associations between various indicators of leisure and well-being among a sample of emerging adults (n = 2,537). This study occurred early during the COVID-19 pandemic and indicators accounted for the context of adversity. As expected, all associations between self-reported indicators, namely subjective experiences (mindfulness, flow) and well-being (meaning in life, post-traumatic growth), were positive and significant, but varied in size. Findings suggest that different processes may be involved in the promotion of well-being. Mindfulness may be helpful to finding meaning in life because the practice can help to identify personal value and avoid judgment about aspects out of one’s control. Flow might be beneficial as a coping mechanism for individuals dealing with adversity. Future research is needed to examine causal relations among indicators.