Causal Relationship Research Articles

Post-Growth Ambitions and Growth-Based Realities in Sustainable Land-Use Planning

Governments have developed, agreed, and often embraced ambitious targets to meet sustainability and climate change demands. The use of land is foundational for long-term success and one of the most crucial resources where absolute limits of development become tangible. In Europe, success in stopping the expansion of settlement uses through building on natural or agricultural land remains limited in scope and speed. While planning instruments could be open for versatile uses, a pro-growth pathway continues at all planning scales. The premise of this article is that growth fixation is inscribed in planning instruments. We build on post-growth planning literature to conceptualize the relevance of (post-)growth for land-use planning. Two examples of planning instruments (modelling regional land use needs, density concepts) and their application in German case studies illustrate wherein growth has been locked and within which potentials for change lie. We investigate inscribed premises of the causal relation between population and household growth to land consumption that are leading to a divergence between the need for land and the provision of land. By doing so, we position post-growth planning to understand contemporary challenges in reducing the net consumption of land, and as a crucial body of thought that better accounts for the tangible limits of available land.

The impact of foreign direct investment on exports in the kingdom of Saudi Arabia (an empirical study)

This study is considered one of the few studies that attempted to explore the relationship between exports and foreign direct investment in the Kingdom of Saudi Arabia. The study aims to determine the nature of the relationship between exports and foreign direct investment in the Kingdom of Saudi Arabia during the period between (1990–2023). Employing Ender’s methodology using cointegration and error correction model. The study also relies on data on Saudi exports and foreign direct investment inflows from the World Bank databases. The results indicate the existence of Cointegration between foreign direct investment (FDI) inflows and the Saudi exports in the period (1990–2023), as for the causal relationship between the two variables, the results showed the causal relation between exports and FDI inflows from the direction of exports only, which means that Saudi exports cause FDI inflows in Saudi Arabia, and the study recommends giving more incentives to attract foreign investors in different sector rather than oil sector, besides improving the logistical services which is vital to any investment attraction strategy.

SEM Approach to the Mediation Analysis of the Two-Condition Within-Subject Design

The effects of the two-condition within-subject (TCWS) conditional mediation model are developed using the SEM approach. The structural equation model for the within-subject mediator and the within-subject outcome with the specification of a between-subject moderator is developed to derive the indirect effects. Formulas are derived for the indirect effects of two conditions in the TCWS conditional mediation model. These effects are calculated on the rotated outcomes, which are the difference and sum of the outcomes, through two mediators. The derivation is done under both natural and rotated condition approaches. The rotated outcome is expressed as a linear function of the two natural mediators in the natural condition approach, whereas in the rotated condition approach, it is expressed as a function of the two rotated mediators. The total indirect effect on each rotated outcome in both natural and rotated condition approaches consists of two individual indirect effects: one through the within-condition mediator and the other through the crossed-condition mediator. Comparison of two individual indirect effects can reveal the nature of causal relations between conditions and outcomes. The TCWS conditional mediation model is generalized by considering multiple within-subject mediators and multiple between-subject moderators as well as the multiple conditions case. The bootstrapping confidence intervals are used to estimate and test the conditional indirect effects. The total and individual conditional indirect effects are probed by overlaying their bootstrap confidence intervals for given moderator values, and this multi-plot helps the understanding of magnitudes and mechanisms of the moderator on the indirect effects.

Automating psychological hypothesis generation with AI: when large language models meet causal graph

Leveraging the synergy between causal knowledge graphs and a large language model (LLM), our study introduces a groundbreaking approach for computational hypothesis generation in psychology. We analyzed 43,312 psychology articles using a LLM to extract causal relation pairs. This analysis produced a specialized causal graph for psychology. Applying link prediction algorithms, we generated 130 potential psychological hypotheses focusing on “well-being”, then compared them against research ideas conceived by doctoral scholars and those produced solely by the LLM. Interestingly, our combined approach of a LLM and causal graphs mirrored the expert-level insights in terms of novelty, clearly surpassing the LLM-only hypotheses (t(59) = 3.34, p = 0.007 and t(59) = 4.32, p < 0.001, respectively). This alignment was further corroborated using deep semantic analysis. Our results show that combining LLM with machine learning techniques such as causal knowledge graphs can revolutionize automated discovery in psychology, extracting novel insights from the extensive literature. This work stands at the crossroads of psychology and artificial intelligence, championing a new enriched paradigm for data-driven hypothesis generation in psychological research.

Earlier false belief understanding predicts later lie‐telling behavior in preschool children, but not vice versa

AbstractYoung children's lie‐telling behavior is associated with their theory of mind (ToM) development. However, current evidence is primarily based on cross‐sectional studies, with very little longitudinal evidence on the causal relation between the two constructs. The current study provided much‐needed cross‐lagged longitudinal evidence on the association between ToM and lying in young children. Adopting a short‐term longitudinal design, we tested 104 normally developing children's (64 boys, M = 54.0 months) false belief understanding and lie‐telling behaviors three times at 4‐month intervals. Results showed the cross‐lagged model fit the data well. Lie‐telling behaviors exhibited moderate stability across the three time points, while ToM exhibited moderate stability between the first two time points but not between Time 2 and Time 3. Earlier false belief understanding significantly predicted children's later lie‐telling behavior, controlling for family socioeconomic status, child age, gender, only child status, and Time 1 verbal ability and inhibitory control. On the contrary, earlier lie‐telling did not predict later false beliefs understanding. We concluded that earlier false belief understanding predicts later lie‐telling behavior in preschool children, but not vice versa.

Role of Macrocalcification in Raising the Suspicion of Malignancy among the ACR TIRADS Thyroid Nodules Detected by Ultrasonography

Abstract Background Thyroid nodules are a frequent finding on neck sonography. The US is the most commonly used imaging technique for the evaluation of thyroid nodules. The Thyroid Imaging, Reporting and Data System (TIRADS) developed by the American college of radiology (ACR) for classification of thyroid nodules is a practical and convenient method for risk stratification of thyroid malignancy, based on allocating points for different sonographic features of thyroid nodules in five morphologic categories (composition, echogenicity, margins, shape, and presence of echogenic foci). Objective To evaluate the role of macrocalcification in raising the suspicious of malignancy among ACR TIRADS 3 and 4 group without macrocalcification Patients and Methods Our study was conducted on 60 adult patients, 20 patients with TIRADS 3 nodule, 20 patients with TIRADS 4 group with macrocalcification nodules and 20 patients group TIRADS 4 without macrocalcification nodules, whose ages range between 23 and 65 years old and were referred to undergo FNA procedures. Results The present study concludes parenchymal macrocalcifications showed no statistically significant difference in raising percentage of malignancy among TIRADS 4 patients. Conclusion We conclude that the added macrocalcification showed no statistically significant difference in raising percentage of malignancy compared to TIRADS 4 without calcification group. Also, presence of macrocalcification should not prevent clinicians from making further assessments in case of nondiagnostic results to exclude their association with rather than their causal relation with thyroid malignancies.

Analysis of the Relationship between Rheumatoid Arthritis and Osteoporosis Based on Mendelian Randomization.

As the global population ages, the World Health Organization has found a yearly increase in the incidence of rheumatoid arthritis and osteoporosis. This trend poses a challenge to public health and healthcare and calls for the implementation of more preventive and treatment measures to address these health issues. This study aims to investigate the causal relationship between rheumatoid arthritis (RA) and osteoporosis (OP) using the Mendelian randomization (MR) method. OP diagnosis was based on the gold standard of bone mineral density (BMD). Single nucleotide polymorphisms (SNPs) were identified from the genome-wide association research database formed by RA and BMD, with a parameter setting of P < 5×10-8, chain imbalance r2<0.01, and kb = 10,000. Five complementary MR methods, including inverse variance weighted (IVW), MR-Egger regression, weighted median, simple mode estimation based on mode, and weighted estimation based on mode, were used to evaluate the causal relationship between RA and OP/BMD using odds ratio (OR) values and 95% confidence intervals (CI). Sensitivity analyses were performed using heterogeneity tests, horizontal pleiotropy, and individual rejection tests. A total of 78 instrumental variables were identified that were closely related to both RA and BMD in mixed populations, while 14 instrumental variables were identified in the European population and 38 instrumental variables were identified in the Asian population. Using IVW as the main analysis method, the MR analysis results of RA and BMD showed the following: mixed population OR = 0.96, 95%CI: 0.93-1.00; European population OR = 0.55, 95%CI: 0.27-1.12; and Asian population OR = 0.95, 95%CI: 0.90-1.01. Sensitivity analyses showed that the MR results were robust. The study found insufficient evidence of a causal relationship between RA and OP/BMD, suggesting that RA may not have a direct effect on OP/BMD.

Causality relations and causality direction of shallots price changes in east java province, Indonesia

Causality relations and causality direction of shallots price changes in east java province, Indonesia

Do signatory auditors with tax expertise facilitate or curb tax aggressiveness?

Prior research concludes that tax-expert auditors facilitate tax aggressiveness. However, these studies examine auditors who also provide non-audit tax services to their clients, creating conflicting incentives. We predict that tax-expert auditors, who do not provide non-audit tax services, reduce tax aggressiveness, because tax aggressiveness imposes costs on them. We test our prediction using Chinese data, allowing us to identify Certified Tax Agents as tax-expert auditors. We find that companies are less tax aggressive when their signatory auditor is a tax-expert who does not provide non-audit tax services. Consistent with a causal relation, a decrease in tax rates, which reduces clients’ incentives to be tax aggressive, weakens the effect of tax-expertise on tax aggressiveness. Moreover, tax-expert auditors attenuate the type of tax aggressiveness that results in tax-related misstatements. Overall, by examining auditors who do not provide non-audit tax services, we find that tax-expert auditors curb tax aggressiveness, contrary to prior research.

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.

Time variability and periodicities of cross‐regional hydroclimatic causation in the contiguous United States

AbstractIdentifying and understanding various causal relations are fundamental to climate dynamics for improving the predictive capacity of Earth system modeling. In particular, causality in Earth systems has manifest temporal periodicities, like physical climate variabilities. To unravel the characteristic frequency of causality in climate dynamics, we develop a data‐analytic framework based on a combination of causality detection and Hilbert spectral analysis, using a long‐term temperature and precipitation dataset in the contiguous United States. Using the Huang–Hilbert transform, we identify the intrinsic frequencies of cross‐regional causality for precipitation and temperature, ranging from interannual to interdecadal time scales. In addition, we analyze the spectra of the physical climate variabilities, including El Niño‐Southern Oscillation and Pacific Decadal Oscillation. It is found that the intrinsic causal frequencies are positively associated with the physics of the oscillations in the global climate system. The proposed methodology provides fresh insights into the causal connectivity in Earth's hydroclimatic system and its underlying mechanism as regulated by the characteristic low‐frequency variability associated with various climatic dynamics.

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.