Indirect Causality Research Articles

Pain, obesity, adenosine salvage disruption, and smoking behavior mediate the effect of gut microbiota on sleep disorders: results from network Mendelian randomization and 16S rDNA sequencing.

The objective of this study is to investigate the indirect causalities between gut microbiota and sleep disorders. In stage 1, we utilized 196 gut microbiota as the exposure factor and conducted a two-sample univariable Mendelian randomization (MR) analysis on five sleep disorders: insomnia, excessive daytime sleepiness (EDS), sleep-wake rhythm disorders (SWRD), obstructive sleep apnea (OSA), and isolated REM sleep behavior disorder (iRBD). In stage 2, we validated the MR findings by comparing fecal microbiota abundance between patients and healthy controls through 16S rDNA sequencing. In stage 3, we explored the indirect pathways by which the microbiota affects sleep, using 205 gut microbiota metabolic pathways and 9 common risk factors for sleep disorders as candidate mediators in a network MR analysis. In stage 1, the univariable MR analysis identified 14 microbiota potentially influencing five different sleep disorders. In stage 2, the results from our observational study validated four of these associations. In stage 3, the network MR analysis revealed that the Negativicutes class and Selenomonadales order might worsen insomnia by increasing pain [mediation: 12.43% (95% CI: 0.47, 24.39%)]. Oxalobacter could raise EDS by disrupting adenosine reuptake [25.39% (1.84, 48.95%)]. Allisonella may elevate OSA risk via obesity promotion [36.88% (17.23, 56.54%)], while the Eubacterium xylanophilum group may lower OSA risk by decreasing smoking behavior [7.70% (0.66, 14.74%)]. Triangulation of evidence from the MR and observational study revealed indirect causal relationships between the microbiota and sleep disorders, offering fresh perspectives on how gut microbiota modulate sleep.

Partial Cross Mapping Based on Sparse Variable Selection for Direct Fault Root Cause Diagnosis for Industrial Processes.

Root cause diagnosis of process industry is of significance to ensure safe production and improve production efficiency. Conventional contribution plot methods have challenges in root cause diagnosis due to the smearing effect. Other traditional root cause diagnosis methods, such as Granger causality (GC) and transfer entropy, have unsatisfactory performance in root cause diagnosis for complex industrial processes due to the existence of indirect causality. In this work, a regularization and partial cross mapping (PCM)-based root cause diagnosis framework is proposed for efficient direct causality inference and fault propagation path tracing. First, generalized Lasso-based variable selection is performed. The Hotelling T2 statistic is formulated and the Lasso-based fault reconstruction is applied to select candidate root cause variables. Second, the root cause is diagnosed through the PCM and the propagation path is drawn out according to the diagnosis result. The proposed framework is studied in four cases to verify its rationality and effectiveness, including a numerical example, the Tennessee Eastman benchmark process, the wastewater treatment process (WWTP), and the decarburization process of high-speed wire rod spring steel.

Root cause analysis of faults in cement pre-decomposition system using kernel principal component analysis and multi-scale symbolic transfer entropy

The industrial production process is complex, and when a fault occurs, multiple variables deviate from their normal state. In this paper, a solution is proposed which utilizes correlation and causality analysis to identify the root cause of failure at an early stage. Kernel principal component analysis (KPCA) demonstrates excellent monitoring performance in nonlinear systems. By applying the unified contribution graph method to KPCA, the main fault variables can be identified. Additionally, the Multi-Scale Symbolic Transfer Entropy (MSTE) method is employed to construct a delay causality diagram, addressing non-stationarity and lack of time-lag analysis. It considers time delays between variables while excluding indirect causality to diagnose the root failure point. The effectiveness of KPCA-MSTE is further validated by analyzing the failure process of a cement predecomposition system in actual production.

The Long-Run Effect of Carbon Emission and Economic Growth in European Countries: A Computational Analysis through Vector Error Correction Model

The paper seeks to examine the association that exists among a number of energy-related variables such as energy use, renewable energy use, carbon pollutants and the economic growth of European Union countries. The examination of variables focus on twenty-one years of data from 2000 to 2020 using a multidimensional data framework. The findings come from empirical analysis carried out using panel VECM model and associated tests such as, panel unit root test, cointegration and the causality one. The different variables indicated above have positive effects on the growth of economies in various EU member states. Results obtained from the use of the heterogeneous causality test indicated that there is an indirect causality between energy use and the rate at which economies develop. Based on the findings obtained from the study, there is a need for EU member states to establish policies that should help to enhance efficiency in energy use to promote economic development.

Mining and modeling the direct and indirect causalities among factors affecting the Urban Heat Island severity using structural machine learned Bayesian networks

Urbanization, population growth, and climate change have several impacts on the environment including the extreme increase in temperature in urban areas, which is also known as the Urban Heat Island (UHI) effect. This paper presents a novel white-box data-driven structural learning Bayesian network model that (1) discovers knowledge from the data by identifying the key factors impacting the UHI severity; (2) captures the causal (direct and indirect) relationships between the different variables that influence UHI severity, and (3) represents the learned relationships into graphical networks that are both machine- and human-interpretable. Different Bayesian networks were developed based on a dataset comprised of 31 meteorological, socio-demographic, geographic, and land use/land cover factors gathered for the State of New Jersey, USA. Furthermore, the different Bayesian networks were assessed and compared to determine the optimal structure. Finally, the best model was validated on an unseen testing sample where an overall accuracy of 88.51% was obtained. The proposed optimal Bayesian network model was able to discover knowledge about 13 causal relationships between 12 variables (one of which is the UHI severity). The outcomes of this research are crucial for urban management and for proposing proper adaptation plans for the UHI effect.

Multiscale Partial Symbolic Transfer Entropy for Time-Delay Root Cause Diagnosis in Nonstationary Industrial Processes

Nonstationary characteristics and lack of time delays analysis remain two large obstacles to diagnosing fault root cause in industrial processes. However, ignoring these two issues, many traditional methods tend to introduce some spurious or indirect causal relationships that interfere with root cause judgments. In this article, multiscale partial symbolic transfer entropy (MPSTE) is proposed to handle the above-mentioned problems. On the one hand, a multivariate multiscale filter module of MPSTE is designed to estimate the information transfer on multiple time scales and detect time delays while considering the indirect causality of the multivariate industrial system. On the other hand, to capture the causal information from the nonstationary processes, MPSTE introduces a segmental symbolization procedure and maps time series into finite rank vectors, overcoming the limitation of stationarity. Moreover, two causal criteria for MPSTE are constructed to identify the direction and significance level of causality correspondingly. MPSTE can thus provide sufficient information for causal relationships and exclude spurious causality. Finally, a causal diagram construction strategy with time delay based on MPSTE is established to diagnose the root cause and trace the fault propagation path. The results of experimental verification in a coal mill rig prove the effectiveness of the proposed strategy.

Reconstructing Marxian Theory of Ground Rent: Based on Japanese Development of Marxian Political Economy

ABSTRACT The aim of this paper is to reconstruct Marxian theory of ground rent in order to refurbish it as a fundamental theoretical framework of ecological Marxism. We make this attempt by the close examination of longstanding Japanese discussions on the rent theory among the Koza school, Rono school, and the Uno school, which have never been made available in English so far. By critically integrating Japanese preceding studies, Marxian theory of rent would be reinforced more effectively in analysing the class relation between the capitalist and the landowner, which is reflected in the formation of absolute rent. In addition, we use the price equations to formulate the rent formation and theoretically examine how the landowner performs as the third class. It is noteworthy that there is a case in which the conflict between the capitalist and the landowner takes the form of a confrontation about rent-seeking activities. In our two-sector model analysis, an intellectual property right in one sector can undermine the economic position of the landowners in the other sector, thereby fostering further destructive development of nature. Capitalist IP rent-seeking harms the environment not only directly but also through indirect causalities in the globally interrelated economy.

Impact of Public Spending in Infrastructure on Influencing Economic Development from East Africa Countries: The Vector Auto Regressive Analysis

Infrastructure development is essential for a country's growth and development. The goal of this study is to look at how public infrastructure expenditure affects economic growth in East Africa. Countries:The Vector Auto Regressive Analysis. The study employed a time series data research approach using secondary data. The population was applied to the financial records from 1984/85 to 2015/16. (Annual Data). The study's sample size was 32 yearly observations. The study's research site was purposefully chosen in Uganda. The data was gathered from a variety of trustworthy sources, including the World Bank. The Johansen co integration test shows a long-run relationship between public investment in infrastructure, communication, and electricity and the rate of economic growth. Simultaneously, the Granger-Causality test revealed indirect causality between economic development rate and all components of public spending used, with a P- Value of 0.04; and the Vector Auto Regressive (VAR) consequences revealed that public spending on infrastructure, communication, and energy had a direct effect on economic development rate. According to the study, increased investment on major infrastructure such as water facilities, airports, highways, power, trains, and communication adds considerably to the rate of economic development by boosting the efficiency of the public and private sectors.

Prior Knowledge-Based Causal Inference Algorithms and Their Applications for China COVID-19 Analysis

Causal inference has become an important research direction in the field of computing. Traditional methods have mainly used Bayesian networks to discover the causal effects between variables. These methods have limitations, namely, on the one hand, the computing cost is expensive if one wants to achieve accurate results, i.e., exponential growth along with the number of variables. On the other hand, the accuracy is not good enough if one tries to reduce the computing cost. In this study, we use prior knowledge iteration or time series trend fitting between causal variables to resolve the limitations and discover bidirectional causal edges between the variables. Subsequently, we obtain real causal graphs, thus establishing a more accurate causal model for the evaluation and calculation of causal effects. We present two new algorithms, namely, the PC+ algorithm and the DCM algorithm. The PC+ algorithm is used to address the problem of the traditional PC algorithm, which needs to enumerate all Markov equivalence classes at a high computational cost or with immediate output of non-directional causal edges. In the PC+ algorithm, the causal tendency among some variables was analyzed via partial exhaustive analysis. By fixing the relatively certain causality as prior knowledge, a causal graph of higher accuracy is the final output at a low running cost. The DCM algorithm uses the d-separation strategy to improve the traditional CCM algorithm, which can only handle the pairwise fitting of variables, and thus identify the indirect causality as the direct one. By using the d-separation strategy, our DCM algorithm achieves higher accuracy while following the basic criteria of Bayesian networks. In this study, we evaluate the proposed algorithms based on the COVID-19 pandemic with experimental and theoretical analysis. The experimental results show that our improved algorithms are effective and efficient. Compared to the exponential cost of the PC algorithm, the time complexity of the PC+ algorithm is reduced to a linear level. Moreover, the accuracies of the PC+ algorithm and DCM algorithm are improved to different degrees; specifically, the accuracy of the PC+ algorithm reaches 91%, much higher than the 33% of the PC algorithm.

Causality extraction model based on two-stage GCN

As one of the indirect causality, cascaded causality can be used to construct the event knowledge graph, causal inference, scenario analysis, etc. The existing GCN methods lack the mining of context information and relevant entity information, resulting in the poor ability of causality inference, which inevitably affects the extraction accuracy of cascade causality. To solve this problem, this paper proposes a causality extraction model based on a two-stage GCN to improve the extraction accuracy. To obtain rich features of entities, this work combines sentiment polarity and knowledge base to get the causality candidate entity library. Firstly, the BERT model is pre-trained using context information and relevant entity information extracted from the entity library to obtain the final entity nodes. Secondly, using the semantic dependency graph, each possible edge between any two entity nodes can be obtained, which are input into the first stage GCN to get a preliminary directed graph of causality. Finally, the directed graph of causality is input into the second stage GCN to achieve deep causality multi-hop inference. Thus, the cascade causality is inferred and extracted by the two-stage GCN model. Experiments show that the extraction accuracy of cascade causality has been further improved.

Forgotten effects of active tourism activities in Spain on sustainable development dimensions

This research aims at exploring the impact of active tourism activities on sustainable development dimensions. The methodology applied was based on the Theory of Forgotten Effects, which involves the use of different matrices as a research method representing relationship between different elements. According to experts’ opinions, direct and indirect relationships effects caused by active tourism on sustainable development dimensions were evaluated. Particularly, the elements in the matrices were, on the one hand, types of active tourism activities by natural setting (ground, underground, water, underwater and aerial) and, on the other hand, dimensions of sustainable development goals (economic, social, environmental, cultural and governmental). The use of the Forgotten Effects Theory allowed us to analyse incidence relationships, both direct causalities identified by experts in the first place, and also significant linkages between elements in the matrices which had been overlooked initially but showed a strong incidence after applying the aforementioned research method (indirect causalities). Besides this identification of overlooked incidences or “forgotten effects”, another remarkable contribution of this pioneer work is in the selection of themes or objects of study included in the fuzzy metrics, as no other work relating active tourism activities and sustainable development dimensions was found in the literature.

A new structural multivariate GARCH-BEKK Model: Causality of green, sustainable and fossil energy ETFs

The article examines exchange-traded funds (ETFs) for green and sustainable energy regarding causality in their asset returns and volatilities. The structural vector autoregressive (VAR) model is one of the popular methodologies for the empirical analysis of macroeconomics and finance. However, the analysis is limited to the conditional mean, and excludes the structural analysis of conditional covariance models which measure the volatility and co-volatility of the financial asset returns. In order to accommodate this limitation, we develop a new structural multivariate GARCH-BEKK model that accommodates a dimension reduction for a BEKK-type parameterization of the time-varying covariance structure. We use a quasi-maximum likelihood estimator, which is shown to have consistency and asymptotic normality. For energy ETF returns, we construct the structural GARCH-BEKK model in order to investigate the causality in returns and volatility via statistical tests and impulse response functions, especially for two events, namely a drop in crude oil prices on May 5, 2011, and the Fukushima nuclear disaster on March 11, 2011. Our empirical results have found that for the portfolio renewable energy ETFs, Solar, Wind, and Water seem to exhibit indirect mutual causality effects in mean, and direct mutual causality effects in the second moment. When we incorporate the oil market into the renewable energy market, Oil seems to dominate the causality effects, so indirect uni- causality effects of the mean from the Oil to the Solar ETF, or Oil to the Water ETF, are found. However, there are no uni-causality or mutual causality effects in the second moment.

Abnormality monitoring and causality analysis based on KF-PDC and IACE in blast furnace ironmaking process

ABSTRACT Blast furnace (BF) ironmaking is a highly complicated process with multi-variable nonlinear coupling and multi-mode characteristics. In this article, a developed kernel function partial derivative contribution (KF-PDC) is proposed for abnormality location, which makes up for the deficiency of linear multivariate statistical process monitoring (MSPM) and de-redundancies the variable candidate set of causality analysis. Then, to eliminate the influence of multi-mode, an online interval adaptive causation entropy (IACE) is established to analyse the cause–effect relationships of candidate abnormal variables, which contributes to distinguishing the direct and indirect causality, and the Haar wavelet based on the sliding window (HWSW) is constructed for the segmentation of different modes online. Finally, a case study using actual industrial BF ironmaking data illustrates that the monitoring method can better capture the abnormal furnace conditions and effectively obtain the root cause and propagation path.

Detecting the Causal Nexus between Particulate Matter (PM10) and Rainfall in the Caribbean Area

In this study, we investigate the interactions between particulate matter that have an aerodynamic diameter less than 10 μm diameter (PM10) and rainfall (RR) in entropy framework. Our results showed there is a bidirectional causality between PM10 concentrations and RR values. This means that PM10 concentrations influence RR values while RR induces the wet scavenging process. Rainfall seasonality has a significant impact on the wet scavenging process while African dust seasonality strongly influence RR behavior. Indeed, the wet scavenging process is 5 times higher during the wet season while PM10 impact on RR is 2.5 times higher during the first part of the high dust season. These results revealed two types of causality: a direct causality (RR to PM10) and an indirect causality (PM10 to RR). All these elements showed that entropy is an efficient way to quantify the behavior of atmospheric processes using ground-based measurements.

Causal Inference in Microbiome Medicine: Principles and Applications.

Microorganisms that colonize the mammalian skin and cavity play critical roles in various physiological functions of the host. Numerous studies have revealed strong associations between the microbiota and multiple diseases. However, association does not mean causation. To clarify the mechanisms underlying microbiota-mediated diseases, research is moving from associative analyses to causation studies. In this article, we first introduce the principles of the computational methods for causal inference, and then discuss the applications of these methods in microbiome medicine. Furthermore, we examine the reliability of theoretically inferred causality by the interventionist framework. Finally, we show the potential of confirmed causality in microbiota-targeted therapy, especially in personalized dietary intervention. We conclude that a comprehensive understanding of the causal relationships between diets, microbiota, host targets, and diseases is critical to future microbiome medicine.

Climate Change and Educational Attainment: The Role of Human Mobility

This paper investigates the potential links between climate change, human capital formation, and internal migration in Africa. In order to do so, a two-sector, world economy model that endogenises education and mobility decisions is developed. This stylised model predicts that negative climatic conditions increase the proportion of people moving internally from rural to urban areas. This in turn leads to a larger share of individuals investing in tertiary education, because the access and returns to education are higher in urban areas. These theoretical predictions are empirically validated by a panel data analysis at the country level and a cross-sectional data analysis at the province level. The empirical validation reveals indirect causality between climate change and education via the moderating variable of internal migration. The research leads to the conclusion that adverse weather changes may have the unexpected effect of increasing educational attainment in African economies. The findings suggest that greater internal mobility can explain this effect.

Revisiting the Nexus between FDI, financial development and economic growth: Empirical evidence from Nigeria

The need for economic development has preoccupied policymakers over the years. Especially the global south such as Nigeria, which is plagued with infrastructural deficit and less foreign direct investment and financial development attraction. This has drawn the attention of stakeholders and government officials for exploration of alternative routes for sustainable development, which is in line with the United Nations Sustainable Development Goals‐8 (UNSDG's). Thus, the current study focuses on the FDI‐led economic growth hypothesis if it holds or not for the case of Nigeria. The present study is distinct from previous studies in terms of scope by incorporation of more covariates, which has seemed to have been overlooked in the FDI argument. To this end, the current study re‐investigates the connection between FDI, financial development, total labour force, gross capital formation, and economic growth using Nigeria as a representation for Africa and Sub‐Saharan states specifically. Annual time‐series data from 1970 to 2018 is adopted for the econometrics analysis. Our study interest variables are foreign direct investment, economic expansion (GDP). The present study inculcates two additional financial development indicators; from the banking, and financial sectors. Using a battery of unit root and stationarity tests the study explores the stationarity properties of the outlined variables. Subsequently, the novel and recent robust Bayer and Hanck (2013) combined cointegration test in conjunction with Pesaran's ARDL bounds test was used to investigate the equilibrium relationship and regression analysis. While the Toda‐Yamamoto Granger causality was used to detect the direction of causality analysis among the variables. Empirical investigation traces a long‐run equilibrium relationship among the variables over the sampled period. Furthermore, empirical results show that FDI influences GDP, which suggests that FDI influences economic growth which is indicative of policymakers. Similarly, empirical outcomes establish a significant relationship between GDP and Financial Development from banking‐sector which is also corroborated in the causality results as an indirect causality is seen running from GCF to the financial sector. These outcomes suggest that FDI and Financial development are good predictors for sustainable economic growth in Nigeria. All aforementioned results have its inherent policy implications which are elucidated in the concluding remark of this study.

Endogeneidad Monetaria en una Economía Dolarizada: Caso Ecuador

La presente investigación detalla las principales características de la endogeneidad monetaria del Ecuador, basándose en las principales vertientes del pensamiento monetario endógeno de la corriente postkeynesiana. Mediante la utilización de un modelo de Vectores Autorregresivos (VAR) y pruebas de causalidad de Granger, se determina que para el periodo 2007 – 2019 la endogeneidad de la oferta monetaria satisface los postulados de la vertiente horizontalista (acomodatista), puesto que se pudo constatar una causalidad unidireccional del crédito bancario a la base monetaria, una causalidad indirecta del crédito bancario a la oferta monetaria y una causalidad unidireccional del PIB real a la oferta monetaria.

How group and perceiver characteristics affect collective blame following counterproductive work behavior

AbstractTwo experimental studies, based on a model using a novel integration of theories, provide evidence that collective blame, the assignment of blame to members of a group besides the member(s) who directly caused a misdeed, in the aftermath of counterproductive work behavior (CWB) is higher when a group of coworkers is perceived as a single entity. Further, the studies consistently show that this relationship is mediated by inferences about both indirect causality and common blameworthy traits among group members. These findings extend prior research, which has not considered mediation in this relationship by dual inferences. Additionally, perceivers’ implicit theories about people's character were found to moderate the inferences they favor, with inferences about indirect causality versus common traits favored by perceivers with implicit theories that people's character is malleable versus fixed, respectively. These findings extend prior research by empirically supporting the notion that the nature of perceivers’ implicit theories about people's character is related to why they perceive a group to be a single entity. The findings, future directions and implications of improved understanding of the assignment of collective blame following CWB are discussed.

Maintaining Behavior for Preventing Disaster (Ecology Themed Tafsir of Perished Nation)

Nowadays, Ecology themed interpretation becomes a popular mode of Qur’anic interpretation. This interpretation has ultimate goal to help human taking care of nature. As a reward, it is expected that nature would not cause disasters that inflict damages to human. In Qur’an, there are a lot of verses encouraging human for doing good in the earth. The approach that we used up till now for natural disasters prevention is approach relating to human efforts that directly affecting nature, such as not litter, illegal logging, etc. From the stories of perished nations in the Qur’an by terrifying disasters, this research tried to take lessons for the sake of conserving nature where we live. This research maps out that there is an indirect causality between human and nature as nature have also unknown section (ghaib) instead of material section that commonly known. This unknown section is a part of nature that obeying doing sujud and reciting tasybih to Allah. This section is related to how we behave to Allah and other human beings. If we behave properly, the nature will be good to us, and vice versa.