Directed Acyclic Graph Research Articles

HRCT findings predict 1-year mortality in patients with acute exacerbation of idiopathic inflammatory myopathies-associated interstitial lung disease

BackgroundAcute exacerbation of idiopathic inflammatory myopathies-associated interstitial lung disease (AE-IIM-ILD) is a significant event associated with increased morbidity and mortality. However, few studies investigated the potential prognostic factors contributing to mortality in patients who experience AE-IIM-ILD. ObjectivesThe purpose of our study was to comprehensively investigate whether high-resolution computed tomography (HRCT) findings predict the 1-year mortality in patients who experience AE-IIM-ILD. MethodsA cohort of 69 patients with AE-IIM-ILD was retrospectively created. The cohort was 79.7 % female, with a mean age of 50.7. Several HRCT features, including total interstitial lung disease extent (TIDE), distribution patterns, and radiologic ILD patterns, were assessed. A directed acyclic graph (DAG) was used to evaluate the statistical relationship between variables. The Cox regression method was performed to identify potential prognostic factors associated with mortality. ResultsThe HRCT findings significantly associated with AE-IIM-ILD mortality include TIDE (HR per 10%-increase, 1.64; 95%CI, 1.29–2.1, p < 0.001; model 1: C-index, 0.785), diffuse distribution pattern (HR, 3.75, 95%CI, 1.5–9.38, p = 0.005; model 2: C-index, 0.737), and radiologic diffuse alveolar damage (DAD) pattern (HR, 6.37, 95 % CI, 0.81–50.21, p = 0.079; model 3: C-index, 0.735). TIDE greater than 58.33 %, diffuse distribution pattern, and radiologic DAD pattern correlate with poor prognosis. The 90-day, 180-day, and 1-year survival rates of patients who experience AE-IIM-ILD were 75.3 %, 66.3 %, and 63.3 %, respectively. ConclusionHRCT findings, including TIDE, distribution pattern, and radiological pattern, are predictive of 1-year mortality in patients who experience AE-IIM-ILD.

Anxiety mediates association between sex and jaw function limitation in temporomandibular disorder patients from China.

The objective of this study is to explore the relationship between sex and jaw function and to test whether anxiety mediates the causal relationship between sex and jaw function in temporomandibular disorders (TMDs) patients. A total of 488 participants with TMD were included in the analysis. Demographic data were collected. Generalized anxiety symptoms and anxiety severity were initially assessed using the GAD-7 questionnaire. And jaw function limitation was measured using the JFLS-8 scale. A directed acyclic graph (DAG) was used in this study to evaluate the hypotheses. Mediation analysis was conducted to explore causality and to calculate the total effect, natural direct effect (NDE) and natural indirect effect (NIE). In TMD patients, there was a significant association between female and jaw function (r = 0.17, p < 0.001), female and anxiety (r = 0.15, p = 0.002), anxiety and jaw function (r = 0.35, p < 0.001). In addition, sex can directly lead to differences in impaired jaw function (NDE: 3.719, 95% CI: 1.619-5.828, p < 0.001), and can also be causally related to jaw function through anxiety (NIE: 1.146, 95% CI: 0.267-2.024, p = 0.011). And the total effect was 4.865 (95% CI, 2.709-7.029, p < 0.001). A causal mechanism was found that anxiety acts as a mediator of sex effects on jaw function. Therefore, psychological factors need to be taken into account in the treatment of female TMD patients. Further clinical trials are needed to explore whether psychotherapy is more beneficial to improve jaw function in female TMD patients.

A linear time algorithm for linearizing quadratic and higher-order shortest path problems

AbstractAn instance of the NP-hard Quadratic Shortest Path Problem (QSPP) is called linearizable iff it is equivalent to an instance of the classic Shortest Path Problem (SPP) on the same input digraph. The linearization problem for the QSPP (LinQSPP) decides whether a given QSPP instance is linearizable and determines the corresponding SPP instance in the positive case. We provide a novel linear time algorithm for the LinQSPP on acyclic digraphs which runs considerably faster than the previously best algorithm. The algorithm is based on a new insight revealing that the linearizability of the QSPP for acyclic digraphs can be seen as a local property. Our approach extends to the more general higher-order shortest path problem.

Extensions of results on phylogeny graphs of degree bounded digraphs

An acyclic digraph in which every vertex has indegree at most i and outdegree at most j is called an (i,j) digraph for some positive integers i and j. The phylogeny graph of a digraph D has V(D) as the vertex set and an edge uv if and only if one of the following is true: (u,v)∈A(D); (v,u)∈A(D); (u,w)∈A(D) and (v,w)∈A(D) for some w∈V(D). A graph G is a phylogeny graph (resp. an (i,j) phylogeny graph) if there is an acyclic digraph D (resp. an (i,j) digraph D) such that the phylogeny graph of D is isomorphic to G. Lee et al. (2017) and Eoh and Kim (2021) studied the (2,2) phylogeny graphs, (1,j) phylogeny graphs, (i,1) phylogeny graphs, and (2,j) phylogeny graphs. Their work was motivated by problems related to evidence propagation in a Bayesian network for which it is useful to know which acyclic digraphs have chordal moral graphs (phylogeny graphs are called moral graphs in Bayesian network theory). In this paper, we extend their work by giving necessary conditions of chordal (i,2) phylogeny graphs. We go further to give necessary conditions of (i,j) phylogeny graphs by listing forbidden induced subgraphs.

Occupational, academic, and personal determinants of wellbeing and psychological distress in residents: results of a survey in Lyon, France.

The mental health of residents is a growing significant concern, particularly with respect to hospital and university training conditions. Our goal was to assess the professional, academic, and psychological determinants of the mental health status of all residents of the academy of Lyon, France. The Health Barometer of Lyon Subdivision Residents (BASIL) is an initiative which consists in proposing a recurrent online survey to all residents in medicine, pharmacy, and dentistry, belonging to the Lyon subdivision. The first of these surveys was conducted from May to July 2022. Participants should complete a series of validated questionnaires, including the Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS), and the Kessler Psychological Distress Scale (K6), respectively, and ad-hoc questions assessing their global health and hospital and academic working conditions. A Directed Acyclic Graph (DAG) analysis was conducted prior to multivariable analyses, to explore the determinants associated with low wellbeing (WEMWBS <43) and high psychological distress (K6 ≥ 13). A total of 904 residents (response rate: 46.7%) participated in the survey. A low level of wellbeing was observed in 23% of participants, and was significantly associated to job strain (OR = 2.18; 95%CI = [1.32-3.60]), low social support (OR = 3.13; 95%CI = [2.05-4.78]) and the experience of very poor university teaching (OR = 2.51; 95%CI = [1.29-4.91]). A high level of psychological distress was identified for 13% of participants, and associated with low social support (OR = 2.41; 95%CI = [1.48-3.93]) and the experience of very poor university teaching (OR = 2.89, 95%CI = [1.16-7.21]). Hospital working conditions, social support, and the perception of teaching quality, were three major determinants of wellbeing and psychological distress among health profession residents. Demographic determinants, personal life and lifestyle habits were also associated. This supports a multilevel action in prevention programs aiming to enhance wellbeing and reduce mental distress in this specific population and local organizational specificities.

Association between COVID-19 severity and tobacco smoking status: a retrospective cohort study using propensity score matching weights analysis

IntroductionThe COVID-19 pandemic continues to be a global threat to public health, with over 766 million confirmed cases and more than 6 million reported deaths. Patients with a smoking history...

A causal trait model for explaining foliar water uptake capacity

AbstractQuestionsPlants largely vary in their capacity for foliar water uptake (FWU), that is, the capacity to increase leaf water content by directly absorbing water from leaf‐wetting events. Climate change will reduce leaf wetting and increase drought events. Therefore, we need a better understanding of the underlying traits and mechanisms that facilitate FWU.LocationSeasonally dry tropical montane grasslands in Brazil (Campos de Altitude).MethodsWe measured FWU and leaf traits related to wettability, surface conductance, water potential and water storage on up to 55 plant species. By using Direct Acyclic Graph theory and Bayesian modelling, we tested how those leaf traits affect FWU.ResultsWe found that stomatal conductance largely explained interspecific variation in FWU, which was also favoured in species with hydrophilic leaves, high cuticular conductance, more negative leaf water potentials, low dry‐matter content, isohydric behaviour, and more elastic cell walls.ConclusionsDue to the existence of trade‐offs, not all species exhibit an optimal combination of traits that favours FWU. Instead, co‐occurring species have achieved a similar capacity for FWU through distinct trait combinations. Consequently, species engaged in FWU may exhibit differential vulnerabilities to climate change as they can cope with drought using other strategies beside FWU.

Association between TB delay and TB treatment outcomes in HIV-TB co-infected patients: a study based on the multilevel propensity score method.

HIV-tuberculosis (HIV-TB) co-infection is a significant public health concern worldwide. TB delay, consisting of patient delay, diagnostic delay, treatment delay, increases the risk of adverse anti-TB treatment (ATT) outcomes. Except for individual level variables, differences in regional levels have been shown to impact the ATT outcomes. However, few studies appropriately considered possible individual and regional level confounding variables. In this study, we aimed to assess the association of TB delay on treatment outcomes in HIV-TB co-infected patients in Liangshan Yi Autonomous Prefecture (Liangshan Prefecture) of China, using a causal inference framework while taking into account individual and regional level factors. We conducted a study to analyze data from 2068 patients with HIV-TB co-infection in Liangshan Prefecture from 2019 to 2022. To address potential confounding bias, we used a causal directed acyclic graph (DAG) to select appropriate confounding variables. Further, we controlled for these confounders through multilevel propensity score and inverse probability weighting (IPW). The successful rate of ATT for patients with HIV-TB co-infection in Liangshan Prefecture was 91.2%. Total delay (OR = 1.411, 95% CI: 1.015, 1.962), diagnostic delay (OR = 1.778, 95% CI: 1.261, 2.508), treatment delay (OR = 1.749, 95% CI: 1.146, 2.668) and health system delay (OR = 1.480 95% CI: (1.035, 2.118) were identified as risk factors for successful ATT outcome. Sensitivity analysis demonstrated the robustness of these findings. HIV-TB co-infection prevention and control policy in Liangshan Prefecture should prioritize early treatment for diagnosed HIV-TB co-infected patients. It is urgent to improve the health system in Liangshan Prefecture to reduce delays in diagnosis and treatment.

Being Bayesian about learning Bayesian networks from ordinal data

In this paper we propose a Bayesian approach for inferring Bayesian network (BN) structures from ordinal data. Our approach can be seen as the Bayesian counterpart of a recently proposed frequentist approach, referred to as the ‘ordinal structure expectation maximization’ (OSEM) method. Like for the OSEM method, the key idea is to assume that each ordinal variable originates from a Gaussian variable that can only be observed in discretized form, and that the dependencies in the latent Gaussian space can be modeled by BNs; i.e. by directed acyclic graphs (DAGs). Our Bayesian method combines the ‘structure MCMC sampler’ for DAG posterior sampling, a slightly modified version of the ‘Bayesian metric for Gaussian networks having score equivalence’ (BGe score), the concept of the ‘extended rank likelihood’, and a recently proposed algorithm for posterior sampling the parameters of Gaussian BNs. In simulation studies we compare the new Bayesian approach and the OSEM method in terms of the network reconstruction accuracy. The empirical results show that the new Bayesian approach leads to significantly improved network reconstruction accuracies.

Causal structure learning for high-dimensional non-stationary time series

Learning the causal structure of high-dimensional non-stationary time series can help in understanding the data generation mechanism, which is a crucial task in machine learning. However, current causal discovery methods for high-dimensional non-stationary time series face several challenges, including the inability to effectively capture non-stationarity, failure to ensure acyclicity of causal graphs, and reliance on subjective threshold definitions, leading to suboptimal algorithm performance. To address these challenges, we introduce a novel Causal Structure Learning model for High-dimensional Non-stationary Time Series (CSL-HNTS). Firstly, this model presents a graph neural network to model the non-stationarity of time series. Secondly, it introduces a novel Directed Acyclic Graph (DAG) sampling method that transforms the space of DAGs into a continuous space, enabling the search for causal graphs within this continuous space to ensure acyclicity. Finally, the model proposes an automatic threshold definition method, without prior knowledge, to convert the weighted adjacency matrix into the Boolean adjacency matrix of the causal graph, thereby avoiding time-consuming postprocessing steps. The proposed approach is validated using simulation datasets and two real datasets, and is benchmarked against current state-of-the-art methods and ablation experiments. The results demonstrate a significant improvement over existing methods, highlighting the efficacy of the proposed model.

Network-based representations and dynamic discrete choice models for multiple discrete choice analysis

In many choice modeling applications, consumer demand is frequently characterized as multiple discrete, which means that consumer choose multiple items simultaneously. The analysis and prediction of consumer behavior in multiple discrete choice situations pose several challenges. In this paper, to address this, we propose a random utility maximization (RUM) based model that considers each subset of choice alternatives as a composite alternative, where individuals choose a subset according to the RUM framework. While this approach offers a natural and intuitive modeling approach for multiple-choice analysis, the large number of subsets of choices in the formulation makes its estimation and application intractable. To overcome this challenge, we introduce directed acyclic graph (DAG) based representations of choices where each node of the DAG is associated with an elemental alternative and additional information such as the number of selected elemental alternatives. Our innovation is to show that the multi-choice model is equivalent to a recursive route choice model on the DAG, leading to the development of new efficient estimation algorithms based on dynamic programming. In addition, the DAG representations enable us to bring some advanced route choice models to capture the correlation between subset choice alternatives. Numerical experiments based on synthetic and real datasets show many advantages of our modeling approach and the proposed estimation algorithms.

A Causal Framework for the Comparability of Latent Variables

Measurement invariance (MI) describes the equivalence of measurement models of a construct across groups or time. When comparing latent means, MI is often stated as a prerequisite of meaningful group comparisons. The most common way to investigate MI is multi-group confirmatory factor analysis (MG-CFA). Although numerous guides exist, a recent review showed that MI is rarely investigated in practice. We argue that one reason might be that the results of MG-CFA are uninformative as to why MI does not hold between groups. Consequently, under this framework, it is difficult to regard the study of MI as an interesting and constructive step in the modeling process. We show how directed acyclic graphs (DAGs) from the causal inference literature can guide researchers in reasoning about the causes of non-invariance. For this, we first show how DAGs for measurement models can be translated into path diagrams used in the linear structural equation model (SEM) literature. We then demonstrate how insights gained from this causal perspective can be used to explicitly model encoded causal assumptions with moderated SEMs, allowing for a more enlightening investigation of MI. Ultimately, our goal is to provide a framework in which the investigation of MI is not deemed a “gateway test” that simply licenses further analyses. By enabling researchers to consider MI as an interesting part of the modeling process, we hope to increase the prevalence of investigations of MI altogether.

Enhancing smart city operation management: Integrating energy systems with a subway synergism hub

This paper is centered on establishing a secure framework for the optimal concurrent operation of a smart city, encompassing transportation, water, heat, electrical, and cooling energy systems. The studied smart city includes the microgrid, smart transportation system (STS), energy hub (EH) and smart grid. In this regard, a subway synergism hub (SSH) as a new non-energy system is added to the smart city with the aim of serving the subway's water, heat, electrical and cooling demands as well as diminishing the operation cost of the smart city. The EH within the SSH cooperated with a desalination unit is considered to supply the subway's stations water demand by using the sea water. The investigation of the optimal allocation of the SSH unit for reducing the cost of smart city operation is also conducted by introducing a novel intelligent priority selection (IPS) analytical algorithm. In comparison to common meta-heuristic algorithms for allocation problems, the accurate optimal solution can be found in low runtime by the IPS algorithm. To achieve an accurate model of the smart city, directed acyclic graph (DAG) based blockchain approach is provided which can enhance the data and energy exchanges security within the smart city. This research paper introduces a security framework deployed in a smart city setting to establish a secure platform for energy transactions. The findings validate the effectiveness of this model and highlight the value of the IPS method. The effectiveness of the suggested approach has been assessed using the smart city system is comprised of various sections, including EVs, smart grid, microgrid, and SSH, demonstrating the credibility and accuracy of this study.

Higher consumption of ultra-processed foods is associated with obesity and abdominal obesity in socially vulnerable Brazilian women.

This study aimed to assess the consumption of ultra-processed foods (UPF) and identify their association with obesity and abdominal obesity in adult women of reproductive age living insituations of social vulnerability in Maceió, Northeastern Brazil. This was a cross-sectional study carried out between October 2020 and May 2021. An anthropometric evaluation was carried out to assess obesity and abdominal obesity. A dietary assessment was also conducted using a 24-h food recall to determine the calorie intake from UPF. To estimate intra-individual variability in food consumption, the probabilistic Multiple Source Method was used. These data in the form of tertiles were used to analyse the association between the consumption of UPF and obesity and abdominal obesity. Logistic regressions were used to analyse the association. A directed acyclic graph (DAG) was created for this analysis. This study included 1702 women of which 53.7% were 31 years old or older, and 74.2% lived in poverty. It identified that 36.5% and 38.1% of the women had obesity and abdominal obesity, respectively, and that an average of 33.8% of calories consumed came from UPF. In the analysis of association guided by the DAG, it was observed that women with a high-calorie intake from UPF had a 1.3 times higher probability of being obese. It was also observed that women with a moderate and high-calorie intake from UPF were 1.4 and 1.3 times more likely, respectively, to have abdominal obesity. Thus, it can be concluded that socially vulnerable women in Brazil have a relatively high consumption of UPF and that this condition increases the probability of obesity in this population group.

A copula-based secured intelligent dynamic-static energy community transportation system for smart cities

This paper addresses a secured co-dynamic model for the energy management of Electrical Vehicles (EVs) within the real community transportation system (RCTS). The proposed model aims to facilitate interoperability among mobile energy resources within the smart city, enabling the RCTS to model the co-dynamic-static transportation systems (TSs) simultaneously. The energy management model within the traffic flow system focuses on dynamic assignment, considering the power consumption associated with the density of moving vehicles. EVs play a key role in economically managing energy in both static and dynamic behaviors within charging stations while aligning with the current traffic flow.To enhance data security within the smart city ecosystem, a directed acyclic graph (DAG)-based decentralized cyber security approach is recommended. This approach ensures that data transactions involving mobile energy resources are secured against cyber-attacks through the use of public, private, and transaction blocks. Additionally, an uncertainty-based copula function is presented to create a precise management environment within the smart city. The results indicate that the proposed model for transportation energy resources tends to reduce energy costs by optimally controlling energy consumption within traffic flow, compared to normal conditions.

Impact of therapeutic plasmapheresis on the duration of organ failure in patients with hypertriglyceridemia-associated acute pancreatitis

BackgroundPlasmapheresis is widely used for severe hypertriglyceridemia-associated acute pancreatitis (HTG-AP) to remove excessive triglycerides from plasma. This study aimed to evaluate whether plasmapheresis could improve the duration of organ failure in HTG-AP patients.MethodsWe analyzed a cohort of patients from a multicenter, prospective, long-running registry (the PERFORM) collecting HTG-AP patients admitted to the study sites within 72 h from the onset of symptoms. This study was based on data collected from November 2020 to March 2023. Patients who had organ failure at enrollment were involved in the analyses. The primary outcome was time to organ failure resolution within 14 days. Multivariable Cox regression model was used to evaluate the association between plasmapheresis and time to organ failure resolution. Directed acyclic graph (DAG) was used to identify potential confounders.ResultsA total of 122 HTG-AP patients were included (median [IQR] sequential organ failure assessment (SOFA) score at enrollment, 3.00 [2.00–4.00]). Among the study patients, 46 underwent plasmapheresis, and 76 received medical treatment. The DAG revealed that baseline serum triglyceride, APACHE II score, respiratory failure, cardiovascular failure, and renal failure were potential confounders. After adjusting for the selected confounders, there was no significant difference in time to organ failure resolution between patients undergoing plasmapheresis and those receiving exclusive medical treatment (HR = 1.07; 95%CI 0.68–1.68; P = 0.777). Moreover, the use of plasmapheresis was associated with higher ICU requirements (97.8% [45/46] vs. 65.8% [50/76]; OR, 19.33; 95%CI 2.20 to 169.81; P = 0.008).ConclusionsIn HTG-AP patients with early organ failure, plasmapheresis was not associated with accelerated organ failure resolution compared to medical treatment but may be associated with more ICU admissions.Trial registration: The PERFORM study was registered in the Chinese Clinical Trial Registry (ChiCTR2000039541). Registered 30 October 2020.

Mapping network connection and direction among symptoms of depression and anxiety in patients with chronic gastritis.

Regarding neurophysiological and developmental findings, anxiety and depression are usual comorbidities of gastritis patients. However, research related to anxiety and depression among chronic gastritis patients was conducted on the disease level while ignoring symptoms. Hence, we rendered the network approach to reveal the symptoms of anxiety and depression among chronic gastritis patients. Three hundred and sixty-nine chronic gastritis patients (female = 139, Mage = 55.87 years) were asked to complete the Self-Rating Anxiety Scale and Self-Rating Depression Scale. Three symptom networks and one directed acyclic graph (DAG) network were formed. First, in the anxiety network of chronic gastritis patients, dizziness was the most influential symptom. In the depression network of chronic gastritis patients, depressed affect and psychomotor retardation were the influential symptoms. Second, panic, easy fatiguability, weakness, palpitation, depressed affect, tachycardia, fatigue, and psychomotor agitation bridged the anxiety-depression network of chronic gastritis patients. Third, DAG networks showed that anxiousness and hopelessness could trigger other symptoms in the anxiety-depression networks of chronic gastritis patients. The current study provided insightful information on patients with chronic gastritis by examining the structures of symptoms.

Distributed matrix computing system for big data

In order to solve the problem of low computing efficiency in big data analysis and model construction, this paper intended to deeply explore the big data analysis programming model, DAG (Directed Acyclic Graph) and other contents, and on this basis, it adopted a distributed matrix computing system Octopus for big data analysis. Octopus is a universal matrix programming framework that provides a programming model based on matrix operations, which can conveniently analyze and process large-scale data. By using Octopus, users can extract functions and data from multiple platforms and operate through a unified matrix operation interface. The distributed matrix representation and storage layer can design data storage formats for distributed file systems. Each computing platform in OctMatrix provides its own matrix library, and it provides a matrix library written in R language for the above users. SymboMatrix provides a matrix interface to OctMatrix that is consistent with OctMatrix. However, SymboMatrix also retains the flow diagram for matrix operations in the process, and it also supports logical and physical optimization of the flow diagram on a DAG. For the DAG computational flow graph generated by SymbolMatrix, this paper divided it into two parts: logical optimization and physical optimization. This paper adopted a distributed file system based on line matrix, and obtained the corresponding platform matrix by reading the documents based on line matrix. In the evaluation of system performance, it was found that the distributed matrix computing system had a high computing efficiency, and the average CPU (central processing unit) usage reached 70%. This system can make full use of computing resources and realize efficient parallel computing.

Efficient Optimization approach in WSN using Multicast Forwarding Scheme in 6LowPAN Environment

The rapidly growing network of devices that are connected known as the Internet of Things, also referred to as IoT, is designed to collect and exchange data. These devices are integrated with sensors, software, and other technologies. An IPv6-based routing system called 6LowPAN was created especially for Internet of Things devices with little resources. However, because multicast packets must be replicated to every multicast group member, the conventional multicast forwarding technique in 6LowPAN may be wasteful. This can result in excessive energy usage and network traffic, particularly in large-scale Internet of Things implementations. An optimization process is presented here using a multicast forwarding scheme. In the proposed forwarding scheme, nodes can send multicast packets up and down at Low Power and Lossy networks over the 6LowPAN environment. Should the sink node be on the root node, it should immediately request that other nodes join the multicast messaging network of things. If otherwise, an ICMPv6 packet will be sent towards the root node in the Destination-oriented Directed-Acyclic Graphs (DODAG) tree, which expands the multicast messages in the first source's interest. In multi-bounce forward straight-line topology, when the DODAG network root is the source of multicast congestion, it exhibits a similar ratio of packet delivery and delay from end to end with a minimal memory overhead. When traffic via multicast originates from the most important rank node in a backward straight-line topology, this paper focuses on the efficiency of the multicast network at 6LowPAN.&#x0D;

Causal diagrams for civil and structural engineers

Causal diagrams are logic and graphical tools that depict assumptions about presumed causal relations. Such diagrams have proven effective in tackling a variety of problems in social sciences and epidemiology research yet remain foreign to civil engineers. Unlike the traditional means of examining relationships via multivariable regression, causal diagrams can identify the presence of confounders, colliders, and mediators. Thus, this paper hopes to introduce the big ideas behind causal diagrams (specifically, directed acyclic graphs (DAGs)) and how to create and apply such diagrams to several civil engineering problems. Findings from the presented case studies indicate that civil engineers can successfully use causal diagrams to improve their understanding of complex causation relations, thereby accelerating research and practical efforts.