This study examines the incorporation of causal inference methods into intelligent entities and examines the benefits of utilizing causal reasoning to improve decision-making procedures. This study entails conducting an experimental evaluation within...
We address the problem of two-variable causal inference without intervention. This task is to infer an existing causal relation between two random variables, i.e.,
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...
Causal inference is perhaps one of the most fundamental concepts in science, beginning originally from the works of some of the ancient philosophers, through today, but also weaved strongly in current work from statisticians, machine learning experts...
Causality has become a powerful tool for addressing the out-of-distribution (OOD) generalization problem, with the idea of invariant causal features across domains of interest. Most existing methods for learning invariant features are based on optimi...
Banks often assume that higher credit limits increase customer default risk because greater exposure appears to imply greater vulnerability. This reasoning, however, conflates correlation with causation. Whether increasing a customer’s credit l...
Transcription control plays a crucial role in establishing a unique gene expression signature for each of the hundreds of mammalian cell types. Though gene expression data have been widely used to infer cellular regulatory networks, existing methods...
In complex systems constrained by multiple factors, it is very important to identify the key influencing factors for mastering the evolution and development law of a system and for obtaining scientific decision-making suggestions or schemes. At prese...
Background/Objectives: Diabetes is a dangerous disease that is accompanied by various complications, including cardiovascular disease. As the global diabetes population continues to increase, it is crucial to identify its causes. Therefore, we predic...
Recently, causal models have gained significant attention in natural language processing (NLP) and computer vision (CV) due to their capability of capturing features with causal relationships. This study addresses Fine-Grained Visual Categorization (...
The internal validity of a causal inference made based on an observational study is often subject to debate. The potential outcomes framework of causal inference stipulates that causal inference is essentially a missing data problem, and we follow th...
Multimodal emotion recognition (MER) aims to enhance the understanding of human emotions by integrating visual, auditory, and textual modalities. However, previous MER approaches often depend on a dominant modality rather than considering all modalit...
Numerous studies are based on the correlation among stock factors, which affects the measurement value and interpretability of such studies. Research on the causality among stock factors primarily relies on statistical models and machine learning alg...
Knowledge tracing predicts students’ future performance based on their historical performance data, which is significant for students’ learning resource recommendation, learning path prediction, and other aspects. Students’ knowledg...
Although causal inference has shown great value in estimating effect sizes in, for instance, physics, medical studies, and economics, it is rarely used in sports science. Targeted Maximum Likelihood Estimation (TMLE) is a modern method for performing...
In observational causal inference studies, unmeasured confounding remains a critical threat to the validity of effect estimates. While proximal causal inference (PCI) has emerged as a powerful framework for mitigating such bias through proxy variable...
Perception in natural environments relies on the integration of information from multiple sensory modalities. Although combining sensory cues can improve perceptual accuracy, sensory signals are inherently noisy and do not always arise from a common...
This paper presents a nonparametric regression model of categorical time series in the setting of conditional tensor factorization and Bayes network. The underlying algorithms are developed to provide a flexible and parsimonious representation for fu...
In tightly coupled Industrial Control Systems (ICS), abnormal disturbances often propagate throughout the process, triggering a large number of time-correlated alarms that exceed the handling capacity of the operator. Consequently, a key challenge is...
The goal of the recommender system is to learn the user’s preferences from the entity (user–item) historical interaction data, so as to predict the user’s ratings on new items or recommend new item sequences to users. There are two...
With the development of 5G, the number of IoT (Internet of Things) devices connected to the Internet will grow explosively. However, due to the vulnerability of the devices, attackers can launch attacks on the vulnerable IoT devices, causing great im...
This article presents a novel and remarkably efficient method of computing the statistical G-test made possible by exploiting a connection with the fundamental elements of information theory: by writing the G statistic as a sum of joint entropy terms...
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, t...
The swine industry is vital to economic stability and household welfare in China and worldwide but remains highly vulnerable to price volatility driven by multiple factors. Capturing the underlying mechanisms of hog price formation is particularly ch...
Legal judgement prediction (LJP) is a crucial part of legal AI, and its goal is to predict the outcome of a case based on the information in the description of criminal facts. This paper proposes a decision prediction method based on causal inference...
The demands for model accuracy and computing efficiency in fault warning scenarios are increasing as high-speed railway train technology continues to advance. The black box model is difficult to interpret, making it impossible for this technology to...
Breast cancer is the most common cancer in the world. With a 5-year survival rate of over 90% for patients at the early disease stages, the management of side-effects of breast cancer treatment has become a pressing issue. Observational, real-world d...
For the decision-making challenge of ensuring vehicle lane-changing safety, this study proposes a context-dependent causal inference-based model for safe lane changes. Emphasizing multi-vehicle interactions within dynamic traffic scenarios, we constr...
The recent focus on the role of epigenetic mechanisms in mental health has led to several studies examining the association of epigenetic processes with psychiatric conditions and neurodevelopmental traits. Some studies suggest that epigenetic change...
Counterfactual reasoning explores what could have happened if the circumstances were different from what actually occurred. As a crucial subtask, counterfactual story generation integrates counterfactual reasoning into the generative narrative chain,...
The prediction of the maturity date of leafy greens in a planting environment is an essential research direction of precision agriculture. Real-time detection of crop growth status and prediction of its maturity for harvesting is of great significanc...
Accurate forecasting of power plant loads is critical for maintaining a stable power supply, minimizing grid fluctuations, and enhancing power market trading mechanisms. However, the data on power plant generation load (hereinafter abbreviated as loa...
Reservoir optimal operation (ROO) has always been a hot issue in the field of water resources management. Analysis of the relationship of optimal control water level and inflow is conducive to understanding and solving ROO under deterministic inflow...
Understanding and improving web performance is essential for enhancing user experience, yet existing approaches remain largely correlation-based and lack causal interpretability. To address this limitation, we propose a causal-inference-driven framew...
Accurate prediction of individual treatment effects in survival analysis is often complicated by the presence of competing risks and the inherent unobservability of counterfactual outcomes. While machine learning models offer improved discriminative...
The relationship between biodiversity and ecosystem function is crucial for understanding the structure and processes of subtropical forest ecosystems. However, the extent to which biodiversity influences subtropical forest biomass remains unclear. T...
Irritable bowel syndrome with diarrhea (IBS-D) is the most prevalent subtype of IBS, characterized by chronic gastrointestinal symptoms in the absence of identifiable pathological findings. This study aims to investigate the molecular mechanisms unde...
The efficient coordination of multi-Unmanned Aerial Vehicle (UAV) systems in the increasingly complex domain of aerial tasks is hampered by significant challenges, including partial observability, low sample efficiency, and difficulties in inter-agen...
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...
Parks play a crucial role in mitigating urban heat island effects, a key challenge for urban sustainability. Park cooling intensity (PCI) mechanisms across varying canopy-layer urban heat island (CUHI) gradients remain underexplored, particularly reg...
Atmospheric nitrogen deposition is a vital component of the global nitrogen cycle, with significant implications for ecosystem health, pollution mitigation, and sustainable development. In the Pearl River Delta (PRD) region of China, high levels of a...
The field of argument analysis has become a crucial component in the advancement of natural language processing, which holds the potential to reveal unprecedented insights from complex data and enable more efficient, cost-effective solutions for enha...
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...
With the acceleration of the Fourth Industrial Revolution, the South Korean government has promoted the Smart Factory Construction Support Project as a core strategy for the digital transformation of manufacturing, particularly targeting small and me...
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...
Background: The exposome (e.g., totality of environmental exposures) and its role in Alzheimer’s Disease and Alzheimer’s Disease and Related Dementias (AD/ADRD) are increasingly critical areas of study. However, little is known about how...
Urban forests play a crucial role in both emitting and absorbing atmospheric pollutants. Understanding the ecological processes of biogenic volatile organic compounds (BVOCs) and secondary organic aerosols (SOAs) and their interactions in urban fores...
Many computational methods have been developed to infer causality among genes using cross-sectional gene expression data, such as single-cell RNA sequencing (scRNA-seq) data. However, due to the limitations of scRNA-seq technologies, time-lagged caus...
Ground-level ozone (
The total phosphorus (TP) concentration is a key water quality parameter for water monitoring and a major indicator of the state of eutrophication in inland lakes. Using remote-sensing to estimate TP concentration is useful, as it provides a synoptic...
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