Causal Model Research Articles

Dissecting shared genetic architecture between depression and body mass index

BackgroundA growing body of evidence supports the comorbidity between depression (DEP) and obesity, yet the genetic mechanisms underlying this association remain unclear. Our study explored the shared genetic architecture and causal associations of DEP with BMI.MethodsWe investigated the multigene overlap and genetic correlation between DEP (N > 1.3 million) and BMI (N = 806,834) based on genome-wide association studies (GWAS) and using the bivariate causal mixture model and linkage disequilibrium score regression (LDSC). The causal association was explored by bi-directional Mendelian randomization (MR). Common risk loci were identified through cross-trait meta-analyses. Stratified LDSC and multi-marker gene annotation analyses were applied to investigate single-nucleotide polymorphisms enrichment across tissue types, cell types, and functional categories. Finally, we explored shared functional genes by Summary Data-Based Mendelian Randomization (SMR) and further detected differential expression genes (DEG) in brain tissues of individuals with depression and obesity.ResultsWe found a positive genetic correlation between DEP and BMI (rg = 0.19, P = 4.07 × 10−26), which was more evident in local genomic regions. Cross-trait meta-analyses identified 16 shared genetic loci, 5 of which were newly identified, and they had influence on both diseases in the same direction. MR analysis showed a bidirectional causal association between DEP and BMI, with comparable effect sizes estimated in both directions. Combined with gene expression information, we found that genetic correlations between DEP and BMI were enriched in 6 brain regions, predominantly in the nucleus accumbens and anterior cingulate cortex. Moreover, 6 specific cell types and 23 functional genes were found to have an impact on both DEP and BMI across the brain regions. Of which, NEGR1 was identified as the most significant functional gene and associated with DEP and BMI at the genome-wide significance level (P < 5 × 10−8). Compared with healthy controls, the expression levels of NEGR1 gene were significant lower in brain tissues of individuals with depression and obesity.ConclusionsOur study reveals shared genetic basis underpinnings between DEP and BMI, including genetic correlations and common genes. These insights offer novel opportunities and avenues for future research into their comorbidities.

Design of Optimal Intervention Based on a Generative Structural Causal Model

Design of Optimal Intervention Based on a Generative Structural Causal Model

A Longitudinal Examination between Chronotype and Insomnia in Youths: A Cross-Lagged Panel Analysis.

Adolescence and young adulthood are transitional periods associated with significant changes and challenges, leading to a heightened vulnerability to sleep disturbances and mental health difficulties. This stage is often associated with an increased preference for eveningness, manifested as a later chronotype. The current study aimed to investigate the directionality of the association between chronotype, based on an individual's sleep-wake behaviour, and insomnia in young people using a two-wave panel design with a 12-month interval. A total of 370 participants aged 15-24 (mean age: 21.0 ± 2.0, 72.7% female) were recruited from local secondary schools and universities. Insomnia symptoms were assessed using the Insomnia Severity Index, while chronotype was measured using the Munich Chronotype Questionnaire. Temporal associations were analysed using a series of cross-lagged panel models. The best fitting and most parsimonious model indicated that a later chronotype at baseline predicts more severe insomnia symptoms at the 12-month follow-up after accounting for autoregressive effects. However, the opposite causal model, where baseline insomnia symptoms predicted the chronotype at the 12-month follow-up, was not supported. These findings suggest that a late chronotype may be a potential risk factor for the development of insomnia in young people, emphasising the importance of considering circadian factors in the prevention and treatment of sleep disturbances among this population.

Model-Based Causal Feature Selection for General Response Types

Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters, Bühlmann, and Meinshausen) is a method for causal feature selection which requires data from heterogeneous settings and exploits that causal models are invariant. ICP has been extended to general additive noise models and to nonparametric settings using conditional independence tests. However, the latter often suffer from low power (or poor Type I error control) and additive noise models are not suitable for applications in which the response is not measured on a continuous scale, but reflects categories or counts. Here, we develop transformation-model (tram) based ICP, allowing for continuous, categorical, count-type, and uninformatively censored responses (these model classes, generally, do not allow for identifiability when there is no exogenous heterogeneity). As an invariance test, we propose tram-GCM based on the expected conditional covariance between environments and score residuals with uniform asymptotic level guarantees. For the special case of linear shift trams, we also consider tram-Wald, which tests invariance based on the Wald statistic. We provide an open-source R package tramicp and evaluate our approach on simulated data and in a case study investigating causal features of survival in critically ill patients. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Pre-pregnancy BMI, gestational weight gain, and telomere length in amniotic fluid: a causal graph analysis.

Previous investigations have suggested a potential association between pre-pregnancy body mass index (BMI) and gestational weight gain (GWG) with telomere length (TL) in various tissues of pregnant women and newborns. Nonetheless, as association does not imply causation, our objective was to investigate the causal connections among pre-pregnancy BMI, GWG, and TL in amniotic fluid. The analysis included 136 mother-child pairs from the Mamma & Bambino cohort, and three causal graph models were developed to depict the interconnections between pre-pregnancy BMI, GWG, and TL. Causal graph analysis was conducted utilizing the do-operator to estimate the causal effect of GWG and the controlled direct effect of pregestational BMI. We revealed that transitioning from non-adequate to adequate GWG had a positive impact on the probability of having "long" TL (i.e., a value greater than the population median) in all three models. When considering the effect of pre-pregnancy BMI, the highest probability of "long" TL was observed in normal weight women with adequate GWG. In contrast, the effect of adequate GWG became minimal among overweight women. These results shed light on the potential causality between pre-pregnancy BMI, GWG, and TL in amniotic fluid, emphasizing the importance of appropriate weight management before and during pregnancy for optimal TL outcomes.

A causal model for acoustic assessment of informal learning environments

Irrelevant and intelligible speeches are the most effective distractive noise sources in open-plan environments. They are inevitable in informal learning environments as group assignments are very common. Informal learning can be defined as any additional learning activity that takes place outside the classroom and is conducted individually and collaboratively on campus. This study will examine the impact of sound-absorbing materials on students' attention in informal learning environments in a causal manner. To examine the effect of materials, 3D models will be designed by changing the sound-absorbing materials, using reverberation time as a reference metric by applying the auralization process. The control and treatment models will be provided. The statistical significance of the impacts will be investigated using pairwise correlations, ANOVA, and multivariate regression methods.

A Survey of Data-Driven Construction Materials Price Forecasting

The construction industry is heavily influenced by the volatility of material prices, which can significantly impact project costs and budgeting accuracy. Traditional econometric methods have been challenged by their inability to capture the frequent fluctuations in construction material prices. This paper reviews the application of data-driven techniques, particularly machine learning, in forecasting construction material prices. The models are categorized into causal modeling and time-series analysis, and characteristics, adaptability, and insights derived from large datasets are discussed. Causal models, such as multiple linear regression (MLR), artificial neural networks (ANN), and the least square support vector machine (LSSVM), generally utilize economic indicators to predict prices. The commonly used economic indicators include but are not limited to the consumer price index (CPI), producer price index (PPI), and gross domestic product (GDP). On the other hand, time-series models rely on historical price data to identify patterns for future forecasting, and their main advantage is demanding minimal data inputs for model calibration. Other techniques are also explored, such as Monte Carlo simulation, for both price forecasting and uncertainty quantification. The paper recommends hybrid models, which combine various forecasting techniques and deep learning-advanced time-series analysis and have the potential to offer more accurate and reliable price predictions with appropriate modeling processes, enabling better decision-making and cost management in construction projects.

Policy Learning for Many Outcomes of Interest: Combining Optimal Policy Trees with Multi-objective Bayesian Optimisation

AbstractMethods for learning optimal policies use causal machine learning models to create human-interpretable rules for making choices around the allocation of different policy interventions. However, in realistic policy-making contexts, decision-makers often care about trade-offs between outcomes, not just single-mindedly maximising utility for one outcome. This paper proposes an approach termed Multi-Objective Policy Learning (MOPoL) which combines optimal decision trees for policy learning with a multi-objective Bayesian optimisation approach to explore the trade-off between multiple outcomes. It does this by building a Pareto frontier of non-dominated models for different hyperparameter settings which govern outcome weighting. The method is applied to a real-world case-study of pricing targetting subsididies for anti-malarial medication in Kenya.

The fly connectome reveals a path to the effectome

A goal of neuroscience is to obtain a causal model of the nervous system. The recently reported whole-brain fly connectome1–3 specifies the synaptic paths by which neurons can affect each other, but not how strongly they do affect each other in vivo. To overcome this limitation, we introduce a combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the ‘effectome’. Specifically, we propose an estimator for a linear dynamical model of the fly brain that uses stochastic optogenetic perturbation data to estimate causal effects and the connectome as a prior to greatly improve estimation efficiency. We validate our estimator in connectome-based linear simulations and show that it recovers a linear approximation to the nonlinear dynamics of more biophysically realistic simulations. We then analyse the connectome to propose circuits that dominate the dynamics of the fly nervous system. We discover that the dominant circuits involve only relatively small populations of neurons—thus, neuron-level imaging, stimulation and identification are feasible. This approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, we provide evidence that fly whole-brain dynamics are generated by a large collection of small circuits that operate largely independently of each other. This implies that a causal model of a brain can be feasibly obtained in the fly.

Effects of interval treadmill training on spatiotemporal parameters in children with cerebral palsy: A machine learning approach

Quantifying individualized rehabilitation responses and optimizing therapy for each person is challenging. For interventions like treadmill training, there are multiple parameters, such as speed or incline, that can be adjusted throughout sessions. This study evaluates if causal modeling and Bayesian Additive Regression Trees (BART) can be used to accurately track the direct effects of treadmill training on gait. We developed a Directed Acyclic Graph (DAG) to specify the assumed relationship between training input parameters and spatiotemporal outcomes during Short Burst Locomotor Treadmill Training (SBLTT), a therapy designed specifically for children with cerebral palsy (CP). We evaluated outcomes after 24 sessions of SBLTT for simulated datasets of 150 virtual participants and experimental data from four children with CP, ages 4–13 years old. Individual BART models were created from treadmill data of each step. Simulated datasets demonstrated that BART could accurately identify specified responses to training, including strong correlations for step length progression (R2 = 0.73) and plateaus (R2 = 0.87). Model fit was stronger for participants with less step-to-step variability but did not impact model accuracy. For experimental data, participants’ step lengths increased by 26 ± 13 % after 24 sessions. Using BART to control for speed or incline, we found that step length increased for three participants (direct effect: 13.5 ± 4.5 %), while one participant decreased step length (−11.6 %). SBLTT had minimal effects on step length asymmetry and step width. Tools such as BART can leverage step-by-step data collected during training for researchers and clinicians to monitor progression, optimize rehabilitation protocols, and inform the causal mechanisms driving individual responses.

Entrepreneurs' contribution to community happiness capital from community-based tourism: A causal relationship model study

This study aims to examine the influence of creative leadership on community happiness capital development, investigate the role of participative management in enhancing community happiness capital, and explore the impact of community happiness capital development on improving quality of life. A quantitative research design was employed, with a sample of 260 entrepreneurs involved in community-based tourism in Phatthalung Province, Thailand. Data were collected through questionnaires, and structural equation modeling was used for hypothesis testing. The results indicate that the proposed model, examining the causal relationship and effect of community happiness capital development, fits the empirical data well (chi-square = 47.775, p-value = 0.563, chi-square/df = 0.955, CFI = 1.000, GFI = 0.973, AGFI = 0.951, RMSEA = 0.000). The findings suggest that these insights can be applied to policy development to support the growth and sustainability of community enterprises, contributing to local economic stimulation.

Key Factors Influencing Sustainable Population Growth: A DEMATEL-ANP Combined Approach

This study highlights the importance of sustainable social demographic growth. It uses a model that combines the Decision-making Trial and Evaluation Laboratory (DEMATEL) with the Analytic Network Process (ANP) to examine key factors affecting this growth and their interactions. The analysis focuses on six critical factors: economic development, education and gender equality, health services, environmental sustainability, immigration policies, and technological advancement. Experiments using government and international organization databases include comparative experiments with deep learning prediction models, ensemble learning models, Causal Inference Models, complex network analysis models, and agent-based models. Comparison metrics cover accuracy, precision, recall, and F1 score. The results indicate that, with a data volume of 4000, the optimized model achieves an accuracy of 0.973, precision of 0.981, recall of 0.969, and an F1 score of 0.89, demonstrating the model's superior performance. The DEMATEL method analyzes the direct relationships among the factors. The results show that economic development and technological advancement have impact scores of 3.91 and 3.43, respectively, indicating their strong influence on other factors and their role in promoting sustainable demographic growth. Education and gender equality, health services, and technological advancement each have impact scores of 3.39, meaning they are significantly affected by other factors and are sensitive in the growth process. Finally, the ANP method is used to calculate the weights of each factor, determining their relative importance in sustainable social demographic growth. The results highlight that economic development level and technological advancement and innovation are core factors influencing sustainable social demographic growth, with significant direct and indirect impacts on other factors and a crucial role in the overall system. These findings provide a scientific basis for formulating relevant policies and interventions, particularly in prioritizing and strengthening economic and technological development strategies. This study offers valuable insights for research in demography, sustainable development, and social policy formulation.

Causal Attention Deep-learning Model for Solar Flare Forecasting

Solar flares originate from the sudden release of energy stored in the magnetic field of the active region on the Sun, but the trigger for flares is still uncertain. Currently, deep-learning-based solar flare prediction models have achieved good results and are widely recognized. However, these models focus more on data correlation rather than causality. An ideal flare prediction model should probe into the causes/triggers of solar flares, and diagnose the precursors of flares rather than just correlation analysis. To extract more informative precursors of flares from magnetograms, while suppressing the interference of confounding factors, a causal attention module is introduced to disentangle causal and confounder features from the input features. To address the problem of imbalanced positive and negative samples in the data set, an adaptive data set split mechanism is proposed. It divides the data set into several balanced subsets of positive and negative samples, and dynamically adjusts the subsets according to the model’s prediction results during the training process. The experimental results demonstrate that our proposed model achieves 4.08%, 8.38%, and 2.19% higher accuracy, true skill score, and area under the receiver operating characteristic curve than the baseline model. Additionally, the class-specific heatmaps by using the gradient-weighted class activation mapping method reveal that our proposed model generally focuses on the polarity inverse line of active regions, well in line with theoretical study.

How rational inference about authority debunking can curtail, sustain, or spread belief polarization.

In polarized societies, divided subgroups of people have different perspectives on a range of topics. Aiming to reduce polarization, authorities may use debunking to lend support to one perspective over another. Debunking by authorities gives all observers shared information, which could reduce disagreement. In practice, however, debunking may have no effect or could even contribute to further polarization of beliefs. We developed a cognitively inspired model of observers' rational inferences from an authority's debunking. After observing each debunking attempt, simulated observers simultaneously update their beliefs about the perspective underlying the debunked claims and about the authority's motives, using an intuitive causal model of the authority's decision-making process. We varied the observers' prior beliefs and uncertainty systematically. Simulations generated a range of outcomes, from belief convergence (less common) to persistent divergence (more common). In many simulations, observers who initially held shared beliefs about the authority later acquired polarized beliefs about the authority's biases and commitment to truth. These polarized beliefs constrained the authority's influence on new topics, making it possible for belief polarization to spread. We discuss the implications of the model with respect to beliefs about elections.

Muscle Volume Adaptations To Resistance Training Drive Changes In Dynamic Strength: A Causal Modeling Approach

Muscle Volume Adaptations To Resistance Training Drive Changes In Dynamic Strength: A Causal Modeling Approach

Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation.

The MRI-derived brain network serves as a pivotal instrument in elucidating both the structural and functional aspects of the brain, encompassing the ramifications of diseases and developmental processes. However, prevailing methodologies, often focusing on synchronous BOLD signals from functional MRI (fMRI), may not capture directional influences among brain regions and rarely tackle temporal functional dynamics. In this study, we first construct the brain-effective network via the dynamic causal model. Subsequently, we introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE). This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic inter-play between structural and effective networks via an ordinary differential equation (ODE) model, which characterizes spatial-temporal brain dynamics. Our framework is validated on several clinical phenotype prediction tasks using two independent publicly available datasets (HCP and OASIS). The experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods.

Neural Representations of Emotions in Visual, Auditory, and Modality-Independent Regions Reflect Idiosyncratic Conceptual Knowledge.

Growing evidence suggests that conceptual knowledge influences emotion perception, yet the neural mechanisms underlying this effect are not fully understood. Recent studies have shown that brain representations of facial emotion categories in visual-perceptual areas are predicted by conceptual knowledge, but it remains to be seen if auditory regions are similarly affected. Moreover, it is not fully clear whether these conceptual influences operate at a modality-independent level. To address these questions, we conducted a functional magnetic resonance imaging study presenting participants with both facial and vocal emotional stimuli. This dual-modality approach allowed us to investigate effects on both modality-specific and modality-independent brain regions. Using univariate and representational similarity analyses, we found that brain representations in both visual (middle and lateral occipital cortices) and auditory (superior temporal gyrus) regions were predicted by conceptual understanding of emotions for faces and voices, respectively. Additionally, we discovered that conceptual knowledge also influenced supra-modal representations in the superior temporal sulcus. Dynamic causal modeling revealed a brain network showing both bottom-up and top-down flows, suggesting a complex interplay of modality-specific and modality-independent regions in emotional processing. These findings collectively indicate that the neural representations of emotions in both sensory-perceptual and modality-independent regions are likely shaped by each individual's conceptual knowledge.

A risk assessment model of spontaneous combustion for sulfide ores using Bayesian network combined with grounded theory

Sulfide ores, collectively referred to as a class of minerals with extensive applications, are increasingly susceptible to spontaneous combustion incidents as the mining environment evolves. The spontaneous combustion of sulfide ores is a complex nonlinear process, and the mechanisms of which remain not fully understood. Consequently, it is challenging to delineate the accident trajectory through traditional accident causation models and subsequently construct a probabilistic model for spontaneous combustion risk. To address this issue, this paper proposed a risk assessment model based on grounded theory and Bayesian network. The advantage of this model is that it employs grounded theory to categorize and organize factors influencing the occurrence of accidents, thereby forming a clear accident trajectory and providing a framework and basis for constructing a probabilistic assessment model. Furthermore, the model presented in this paper introduces the D number theory and the Noisy-OR model to handle uncertainties in the risk assessment process and to establish more rational conditional probability tables. The findings suggest that environmental temperature, heat dissipation conditions, safety awareness, and safety skills are the most influential factors in spontaneous combustion incidents when considering a particular point in time. However, when considering the entire process of sulfide ore pile spontaneous combustion, the temperature of the ore pile is the most critical accident factor to monitor. Therefore, the key to suppressing the spontaneous combustion of sulfide ores lies in reducing environmental temperatures, enhancing heat dissipation, and elevating the safety awareness of relevant personnel.

Constructing and refining a model of antecedent mechanisms of parental educational anxiety in the context of General and Vocational Segregation: A mixed study based on an exploratory sequential design

General and vocational education segregation at the high school level has long been a hot topic in China's education system. To contribute to addressing parental educational anxiety in such a context by enhancing its causal mechanism model, this study utilized an exploratory time-series research design and a mixed research method. Initially, in-depth interviews were conducted, followed by coding analysis. They revealed that parents' educational anxiety is characterized by "insufficient information and elevated anxiety", and its main factors were identified as the parents' level of policy understanding, environmental modeling, family support, and perception of educational fairness. A questionnaire was then created to investigate educational anxiety as a variable, and the results were analyzed using fuzzy set qualitative comparative analysis to conduct a configurational analysis. Parental educational anxiety was categorized into three fundamental modes: the policy-information-driven mode, the information-consumption-driven mode, and the subject-information-driven mode. The results of the analysis indicate that the level of parents' understanding of educational policies influences their educational anxiety level, their orientation on media communication affects their recognition of the educational ecology, and their image of vocational education exacerbates their internal anxiety.

THE DEVELOPMENT OF LEARNING KITS BASED ON CAUSALITY MODEL TO IMPROVE CREATIVITY THINKING SKILLS MOMENTUM AND IMPULSE OF STUDENTS

Teachers still do not fully pay attention to the creative thinking abilities of students. This is seen from the selection of models in the teaching tools used by teachers in teaching. The selection of teaching models that tend to be directed towards direct teaching results in students being less active in following physics learning. Therefore, this research aims to determine the feasibility of a causality model learning device to improve creative thinking skills on the material of momentum and impulse. This type of research includes research and development (R&amp;D), using a 4D model design. However, in this research, only the development stage is carried out. The research procedures carried out refer to the steps of developing the 4D model, namely define, design, and develop. The developed learning device consists of a syllabus, lesson plans, and pre-tasks. Data collection is done through validation of the learning device by validators. The results of the validation of the learning device reached an average validation value of 87.50% with the criteria of very valid and the reliability score is 92.30%. Based on these results, it can be concluded that the causality model learning device to improve the creative thinking skills of momentum and impulse students is suitable to be used in learning.