Causal Model Research Articles

Local Government Tourism Officer satisfaction with the Smart Destination model: A case study with the Kano method

The aim of this research is to analyse local government tourism officer satisfaction with a smart destination model using the Kano method. A causal model is proposed with four antecedent variables of tourism officer satisfaction with the smart destination model: perceived usefulness, perceived ease of use, trust in and challenges of the model. The smart destination model applied to a smart destination network in the Valencian Community (Spain) is taken as a case study. The results show that trust, ease of use and challenges have a linear relationship with satisfaction, while usefulness has a non-linear relationship. However, structural equation modelling analysis shows that only trust in the model significantly influences local government tourism officer satisfaction with the model.

Effects of myopia on health-related quality of life in adolescents: a population-based cross-sectional causal empirical survey

ObjectiveMyopia is a prevalent disease affecting adolescent vision and harms various health outcomes. However, the evidence about the effects of myopia on adolescents’ overall health status is limited. Using health-related...

A causal network model to estimate the cardiotoxic effect of oncological treatments in young breast cancer survivors

A causal network model to estimate the cardiotoxic effect of oncological treatments in young breast cancer survivors

Measuring daily tourism mobility spillover at the intra-metropolis level with mobile positioning data

ABSTRACT Although tourism mobility spillover continues to be a key indicator for tourism management, more innovative research must be conducted at the micro level and high sampling frequency. Against the backdrop of an increasing number of global cities, in this paper, we evaluate the daily tourism mobility spillover inside a worldwide city of China: Shanghai. Based on the Granger causal network model and an original mobile positioning dataset, we analyse the causal relationship between local tourism flows and the spillover effects of tourism mobility within Shanghai. By categorising tourists into ‘local tourists from Shanghai’ and ‘tourists from out of Shanghai’, we reveal a significant causal relationship between Shanghai districts and flows generated by ‘tourists from out of Shanghai’. The analysis of the causal network structure also reveals key districts and points of interest that significantly contribute to congestion in tourism mobility and Shanghai's dynamics. This econometric approach offers policymakers a valuable tool to monitor mobility drivers and optimise flows within the city.

The moderating effects of environmental and regulatory quality on financial development to promote sustainable FDI inflows in Canada

Stimulating sustainable FDI through the connection of financial development and its moderating relationships with environmental quality and regulatory quality emerges as a crucial agenda nowadays. This study investigates into this relationship using quarterly data from 1990Q1 to 2022Q4 for Canada; employing ARDL bound tests, Granger Causality, and FM-OLS econometric models. Foreign direct investment is the dependent variable of this study. The findings confirm significant long-run relationships among financial development, stock market development, and the moderating effects of environmental and regulatory quality on FDI inflows in the Canadian economy. Conversely, in the short run, financial development, stock market development, and economic growth exhibit bidirectional causal links with FDI, while environmental quality, regulatory quality, and trade openness demonstrate unidirectional causal links with FDI. The error correction mechanism indicates that all variables quickly return to equilibrium except trade openness. Robustness checks further confirm that all the variables have fully modified co-integrating relationship with FDI inflows including the moderating effects of environmental and regulatory quality which is the innovation in the FDI-Growth existing literature. Thus, policymakers are urged to prioritize environmental quality and regulatory quality, alongside other significant explanatory variables identified in this study to promote sustainable FDI inflows.

Case-control study on associations of hemorrhagic bowel syndrome in swine with feed characteristics and intestinal pathogens

Haemorrhagic bowel syndrome (HBS) is one of the most common causes of death in fattening pigs worldwide. The objective of this descriptive study was to systematically assess predictors or causal components for the appearance of HBS using case farms (mortality rate caused by HBS ≥ 1.5%) in comparison with control farms (mortality rate caused by HBS ≤ 0.25%), focusing on feed ingredients, feed quality and size, and gastrointestinal pathogens. The inclusion of sugar beet as a feed component in liquid feeding systems was found to be associated (p = 0.03) with farms identified as HBS cases. Another predictive or causal factor found for liquid feeding systems, but only for those using meal, was particle size. A higher percentage of small particles (< 2 mm) in the meal was associated with a higher risk of being an HBS case farm (p = 0.02), while no relevant association was detected for the use of pellets. Sugar beet in the diet was also associated with the incidence of HBS.The microbial quality of the feed in dry feeding systems, specifically the number of total aerobes at the first and last outlet tubes, was associated with a higher incidence of HBS (p = 0.03). Faecal sample analysis showed a difference (p < 0.05) in the prevalence of B. pilosicoli in the herd category (case vs. control herd). In this descriptive study, five predictive or causal factors were identified for an HBS farm with a mortality rate due to HBS ≥ 1.5%. These included the number of aerobes in dry matter samples from the first and last feeders, the particle diameter of the meal used in liquid feeding systems and sugar beet as a component of liquid feeding rations, and the presence of B. pilosicoli as an infectious agent at animal level. Relevant associations reinforce the findings of the previously published Swiss study that HBS is a multifactorial syndrome involving different aspects of pig production and cannot be attributed to a single cause. Further studies are needed to develop evidenced based causal models for HBS in swine.

What Explains Hop Growers' Fungicide Use Intensity and Management Costs in Response to Powdery Mildew?

Methods for causal inference from observational data are common in human disease epidemiology and social sciences but are used relatively little in plant pathology. We draw upon an extensive data set of the incidence of hop plants with powdery mildew (caused by Podosphaera macularis) collected from yards in Oregon from 2014 to 2017 and associated metadata on grower cultural practices, cultivar susceptibility to powdery mildew, and pesticide application records to understand variation in and causes of growers' fungicide use and associated costs. An instrumental causal forest model identified growers' spring pruning thoroughness, cultivar susceptibility to two of the dominant pathogenic races of P. macularis, network centrality of yards during May-June and June-July time transitions, and the initial strain of the fungus detected as important variables determining the number of pesticide active constituents applied by growers and the associated costs they incurred in response to powdery mildew. Exposure-response function models fit after covariate weighting indicated that both the number of pesticide active constituents applied and their associated costs scaled linearly with the seasonal mean incidence of plants with powdery mildew. Although the causes of pesticide use intensity are multifaceted, biological and production factors collectively influence the incidence of powdery mildew, which has a direct exposure-response relationship with the number of pesticide active constituents that growers apply and their costs. Our analyses point to several potential strategies for reducing pesticide use and costs for management of powdery mildew on hop. We also highlight the utility of these methods for causal inference in observational studies.

How Do We Analyze the Accident Causation of Shield Construction of Water Conveyance Tunnels? A Method Based on the N-K Model and Complex Network

In the construction of water conveyance tunnels with the shield method, accidents have occurred from time to time, such as collapses and explosions, and it is of practical significance to explore the cause mechanism of the accident. However, previous research has not considered the effects of dependence between risks on the risk spread. In response, we propose a method based on the Natural Killing Model (the N-K Model) and complex network theory to analyze the cause of shield construction accidents in water conveyance tunnels. By deeply exploring the transmission mechanism and action intensity between system risks, this method can scientifically clarify the accident cause mechanism and provide support for engineering construction safety management. The method constructs a risk index system. Secondly, we introduce the N-K model to reveal the risk coupling mechanism. Then, based on complex network theory, we construct the incident causation model and revise the node’s centrality with the coupling value. Finally, the network topology parameters are calculated to quantitatively describe the causal characteristics of accidents, revealing the risk evolution process and critical causes. The research results indicate that the key causes of accidents are failure to construct according to regulations, inadequate emergency measures, poor ability of judgment and decision-making, and insufficient understanding of abnormal situations. The front end of critical links is subject to human or management risks and should be carefully controlled during construction.

Unveiling the shared genetic architecture between testosterone and polycystic ovary syndrome

Testosterone (T) is a critical predictor of polycystic ovary syndrome (PCOS) but the genetic overlap between T and PCOS has not been established. Here by leveraging genetic datasets from large-scale genome-wide association studies, we assessed the genetic correlation and polygenic overlap between PCOS and three T-related traits using linkage disequilibrium score regression and the bivariate causal mixture model methods. The conjunctional false discovery rate (conjFDR) method was employed to identify shared causal variants. Functional annotation of variants was conducted using FUMA. Total T and bioavailable T exhibited positive correlations with PCOS, while sex hormone-binding globulin (SHBG) showed a negative correlation. All three traits demonstrated extensive genetic overlap with PCOS, with a minimum of 68% of T-related variants influencing PCOS. The conjFDR revealed 4 to 6 causal variants within joint genomic loci shared between PCOS and T-related traits. Functional annotations suggested that these variants might impact PCOS by modulating nearby genes, such as FSHB. Our findings support the hypothesis that PCOS is significantly influenced by androgen abnormalities. Additionally, this study identified several causal variants potentially involved in shared biological mechanisms between PCOS and T regulation.

Can “ Zero waste city” policy promote green technology? Evidence from econometrics and machine learning

The promotion of green technology innovation (GTI) is regarded as an effective way to protect the environment and achieve sustainable development. The “Zero waste city” construction pilot policy (ZWCP), is an important policy for the promotion of waste management, and the achievement of sustainable development and circular economy. The paper explores the effect of ZWCP on GTI using the difference-in-differences model and machine learning. The result shows that ZWCP significantly promotes GTI. In addition, causal forest modeling, a type of machine learning method, similarly validates this result. The mechanism effect suggests that ZWCP promotes GTI by increasing research expenditure and enhancing informational development. The heterogeneity effect suggests that ZWCP is more powerful in promoting GTI when it is implemented in western, low administrative level, and resource-based cities. The paper provides a reference for waste management policies and development of GTI in other countries.

Mapping causal pathways with structural modes fingerprint for perovskite oxides

Abstract Causality is innate to determination of fundamental mechanism controlling any physical phenomena. However, combining causality within the standard practices of computational modeling to understand structure-functionality connections is extremely rare. This work proposes a fingerprint based on key structural modes for ABO3-type perovskite oxides and its derivatives, combined with causal models, for predicting Kohn-Sham energies. Our study of causal models capture the inherent coupling between structural modes such as rotation, tilt and antiferroelectric displacements, responsible for phase transition, polarization, magnetization and metal-insulator transition, exhibited by these materials. Although developed for modeling specific functionality, this method is universally applicable to derive other functionalities and even different material classes while tracking hidden causal mechanisms via structural distortions.&amp;#xD;

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.