This article examines the challenges and opportunities in extracting causal information from text with Large Language Models (LLMs). It first establishes the importance of causality extraction and then explores different views on causality, including...
Granger causality provides a framework that uses predictability to identify causation between time series variables. This is important to policymakers for effective policy management and recommendations. Granger causality is recognized as the primary...
In this study, causalities of COVID-19 across a group of seventy countries are analyzed with effective transfer entropy. To reveal the causalities, a weighted directed network is constructed. In this network, the weights of the links reveal the stren...
The goal of this study is to detect linear and nonlinear causal pathways toward climate change as measured by changes in global mean surface temperature and global mean sea level over time using a data-based approach in contrast to the traditional ph...
Granger causality (GC) is a popular method in causal linkage recovery and has been applied to various fields, such as economics and neuroscience. While the conventional Granger causality model is capable of identifying symmetrical causal relationship...
Policymaking requires an in-depth understanding of the cause-and-effect relationships between the sustainable development goals. However, due to the complex nature of socio-economic and environmental systems, this is still a challenging task. In the...
With the widespread application of blockchain technology across various industries, detecting and analyzing performance bottlenecks is crucial for evaluating and optimizing blockchain system performance. However, current research needs general perfor...
This paper develops a data-driven fault tree methodology that addresses the problem of the fault prognosis of an aging system based on an interpretable time causality analysis model. The model merges the concepts of knowledge discovery in the dataset...
This paper presents the results of an analysis of causality detection in a multi-loop control system. The investigation focuses on application of the Transfer Entropy method, which is not commonly used during the exact construction of information and...
In this report, we propose a novel framework to explore the activity interactions and temporal dependencies between activities in complex video surveillance scenes. Under our framework, a low-level codebook is generated by an adaptive quantization wi...
This work investigates the inter-relationships among stream water quality indicators, hydroclimatic variables (e.g., precipitation, river discharge), and land characteristics (e.g., soil type, land use), which is crucial to developing effective metho...
Accurate automobile-demand forecasting can provide effective guidance for automobile-manufacturing enterprises in terms of production planning and supply planning. However, automobile sales volume is affected by historical sales volume and other exte...
An organization’s human resource management practices are essential for its competitive advantage. This study specifically examined human resource (HR) practices that predict corporate performance (employee turnover and firm sales) based on a b...
China is the second largest aviation country in the world. The Chinese aviation industry and economy both developed quickly in the last two decades. However, the interaction mechanisms of aviation and the regional economy were different in each provi...
This study aims to explore the causal relationship between fiscal deficit (FD) and current account deficit (CAD) along with policy recommendations based on long-run and short-run dynamics and sensitivities. A panel data span from 1990 to 2019 is anal...
A construction defect can cause schedule delay, cost overrun and quality deterioration. In order to minimize these negative impacts of construction defects, this paper aims to analyze the causality of construction defects. Specifically, association r...
It is interesting to identify the reasons and the direction of the correlation between the input/output prices and the macro/micro parameters in animal production processes. In the present study, the time series of the monthly data between the years...
Background/Objectives: A bidirectional association between inflammatory bowel disease (IBD) and periodontitis has been observed, yet their causal relationship remains unclear. This study aimed to investigate the potential causal links between these t...
Sinter ore quality directly affects the stability of blast furnace production. In terms of both physical and chemical properties, the main indicators of sinter quality are the TFe content, alkalinity, and drum index. By analyzing the massive historic...
The efficient management of aviation safety requires the precise analysis of trends in incidents. While classical statistical models often rely on the autocorrelation of indicator sequences for trend fitting, significant room remains for performance...
Rooted in dynamic systems theory, convergent cross mapping (CCM) has attracted increased attention recently due to its capability in detecting linear and nonlinear causal coupling in both random and deterministic settings. One limitation with CCM is...
This work presents a knowledge discovery approach through Causal Bayesian Networks for understanding the conditions under which the performance of an optimization algorithm can be affected by the characteristics of the instances of a combinatorial op...
Crude oil draws attention in recent research as its demand may indicate world economic growth trend in the post-COVID-19 era. In this paper, we study the dynamic lead–lag relationship between the COVID-19 pandemic and crude oil future prices. We perf...
In order to prevent safety risks, control marine accidents and improve the overall safety of marine navigation, this study established a marine accident prediction model. The influences of management characteristics, environmental characteristics, pe...
Flight Operations Quality Assurance (FOQA) is an internationally recognized solution to ensure the safety of civil aircraft flights based on Quick Access Recorder (QAR) data. The traditional approach to anomaly detection in civil aviation is to detec...
Large numbers of quantitative trait loci (QTL) affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty in identif...
Fatigue driving of freight vehicles is a major threat to transport safety, often causing heavy casualties and property losses. However, existing studies only focus on superficial correlations between fatigue driving and influencing factors, failing t...
We adopted a weight of evidence approach to establish a causal analysis of an impaired land ecosystem on a regional scale; namely, Daye, a traditional mining city in China. Working processes, including problem statements, a list of candidate causes,...
Causality analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing causality as a real physical notion so as to formulate it fro...
In the context of economic globalization, waterborne transportation plays an important role in international trade and logistics. However, waterborne traffic accidents pose a severe threat to life, property safety, and the environment. To gain a deep...
Pavement performance prediction serves as a core basis for maintenance decision-making. Although numerous studies have been conducted, most focus on road segments and aggregate indicators such as IRI and PCI, with limited attention to the daily deter...
Over the past few years, online learning has exploded in popularity due to the potentially unlimited enrollment, lack of geographical limitations, and free accessibility of many courses. However, learners are prone to have poor performance due to the...
Missing data problems arise in randomized trials, which complicates the inference of causal effects if the missing mechanism is non-ignorable. We tackle the challenge of identifying and estimating the complier average causal effect parameters under n...
This study investigated the relationships between air pollutants (PM10, SO2, NO2, O3, CO) and meteorological parameters (wind speed, relative humidity, ambient temperature) across urban, suburban, and industrial areas in Malaysia from 2017 to 2021. U...
This study investigates the time-varying causal relationship between China’s economic policy uncertainty (EPU) and housing prices (HP) at the macroeconomic level. Using sub-sample rolling-window techniques on monthly nationwide data spanning Ja...
Detecting causal interrelationships in multivariate systems, in terms of the Granger-causality concept, is of major interest for applications in many fields. Analyzing all the relevant components of a system is almost impossible, which contrasts with...
Maritime accidents are low-probability, high-consequence events, making mechanism analysis crucial for risk mitigation. Existing studies often focus on single scenarios or factors and frequently mix pre-incident observational data with subjective uns...
Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are neurodevelopmental disorders with lifelong functional implications. Their potential role as emerging risk factors for cardiovascular diseases (CVDs) is increasingl...
Hysteresis in organic field-effect transistors is attributed to the well-known bias stress effects. This is a phenomenon in which the measured drain-source current varies when sweeping the gate voltage from on to off or from off to on. Hysteresis is...
This paper presents the use of Copula-based deep learning with Horvitz–Thompson (HT) weights and inverse probability of treatment weighting (IPTW) for estimating propensity scores in causal inference problems. This study compares the performanc...
30 September 2024
Modern mediation analysis techniques supplement the primary intention-to-treat analysis with the aim to shed light onto the treatment mechanism. We investigate to what extent the anatomic marker vitreomacular adhesion resolution (VMAR) mediates visio...
Background/Objectives: While some dietary indices have been developed to assess diet quality and chronic disease risk, their comparative effectiveness within the same population remains unclear due to methodological limitations in observational studi...
Rockbursts are one of the most critical geomechanical hazards during the construction of deep tunnels under high in situ stress conditions, as they can compromise worker safety, damage infrastructure, and disrupt excavation continuity. Despite extens...
Future changes are expected in precipitation under climate change, therefore, changes are projected in the oceanic and terrestrial components. However, it remains poorly elucidated how the El Niño–Southern Oscillation (ENSO) can influenc...
Network analysis is an important approach to explore complex brain structures under different pathological and physiological conditions. In this paper, we employ the multivariate inhomogeneous polynomial kernel Granger causality (MKGC) to construct d...
Recently, the safety issue of maritime autonomous surface ships (MASS) has become a hot topic. Preliminary hazard analysis of MASS can assist autonomous ship design and ensure safe and reliable operation. However, since MASS technology is still at it...
In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and per...
In recent years, there has been intense debate in the literature as to whether the Atlantic Multidecadal Oscillation (AMO) is a genuine representation of natural climate variability or is substantially driven by external factors. Here, we perform an...
Due to the lack of training data and effective haze disaster prediction model, the research on causality analysis and the risk prediction of haze disaster is mainly qualitative. In order to solve this problem, a nonlinear dynamic prediction model of...
Ecological multivariate systems offer a suitable data set on which to apply recent advances in information theory and causality detection. These systems are driven by the interplay of various environmental factors: meteorological and hydrological for...
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