Search Results (4)

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 framework for diagnosing and optimizing user-centric Web Vitals such as Largest Contentful Paint (LCP), First Input Delay (FID), and Cumulative Layout Shift (CLS). Our contributions are threefold. (1) We construct a comprehensive feature representation that captures Document Object Model (DOM) structure, resource loading behaviors, rendering characteristics, and JavaScript execution, integrating browser-level domain knowledge into the modeling pipeline. (2) We introduce a hybrid causal discovery method that combines constraint-based reasoning with differentiable score-based learning to estimate high-dimensional causal structures reflecting real rendering processes. (3) We develop a causal-effect-based intervention optimization module that leverages counterfactual reasoning to identify actionable modifications for performance improvement. Our framework further leverages structural symmetries inherent in rendering processes, using repeated layout patterns and invariant dependency flows to reduce redundancy and strengthen the stability and identifiability of causal discovery. Extensive experiments on HTTP Archive, Chrome UX Report (CrUX), and a synthetic ground truth dataset demonstrate that our framework achieves higher causal accuracy, more stable predictive performance, more effective intervention recommendations, and improved interpretability compared with existing rule-based, statistical, and machine learning baselines. These results highlight the potential of causality-aware analysis for practical web performance optimization. Full article

Most mainstream statistical models will achieve poor performance in Out-Of-Distribution (OOD) generalization. This is because these models tend to learn the spurious correlation between data and will collapse when the domain shift exists. If we want artificial intelligence (AI) to make great strides in real life, the current focus needs to be shifted to the OOD problem of deep learning models to explore the generalization ability under unknown environments. Domain generalization (DG) focusing on OOD generalization is proposed, which is able to transfer the knowledge extracted from multiple source domains to the unseen target domain. We are inspired by intuitive thinking about human intelligence relying on causality. Unlike relying on plain probability correlations, we apply a novel causal perspective to DG, which can improve the OOD generalization ability of the trained model by mining the invariant causal mechanism. Firstly, we construct the inclusive causal graph for most DG tasks through stepwise causal analysis based on the data generation process in the natural environment and introduce the reasonable Structural Causal Model (SCM). Secondly, based on counterfactual inference, causal semantic representation learning with domain intervention (CSRDN) is proposed to train a robust model. In this regard, we generate counterfactual representations for different domain interventions, which can help the model learn causal semantics and develop generalization capacity. At the same time, we seek the Pareto optimal solution in the optimization process based on the loss function to obtain a more advanced training model. Extensive experimental results of Rotated MNIST and PACS as well as VLCS datasets verify the effectiveness of the proposed CSRDN. The proposed method can integrate causal inference into domain generalization by enhancing interpretability and applicability and brings a boost to challenging OOD generalization problems. Full article

Cross-domain named entity recognition (NER) is a crucial task in various practical applications, particularly when faced with the challenge of limited data availability in target domains. Existing methodologies primarily depend on feature representation or model parameter sharing mechanisms to enable the transfer of entity recognition capabilities across domains. However, these approaches often ignore the latent causal relationships inherent in invariant features. To address this limitation, we propose a novel framework, the Causal Structure Alignment-based Cross-Domain Named Entity Recognition (CSA-NER) framework, designed to harness the causally invariant features within causal structures to enhance the cross-domain transfer of entity recognition competence. Initially, CSA-NER constructs a causal feature graph utilizing causal discovery to ascertain causal relationships between entities and contextual features across source and target domains. Subsequently, it performs graph structure alignment to extract causal invariant knowledge across domains via the graph optimal transport (GOT) method. Finally, the acquired causal invariant knowledge is refined and utilized through the integration of Gated Attention Units (GAUs). Comprehensive experiments conducted on five English datasets and a specific CD-NER dataset exhibit a notable improvement in the average performance of the CSA-NER model in comparison to existing cross-domain methods. These findings underscore the significance of unearthing and employing latent causal invariant knowledge to effectively augment the entity recognition capabilities in target domains, thereby contributing a robust methodology to the broader realm of cross-domain natural language processing. Full article

Some authors have proposed that research on cognitive differences, including differences between ethnic and racial groups, needs to be prevented because it produces true knowledge that is dangerous and socially undesirable. From a consequentialist perspective, this contribution investigates the usually unstated assumptions about harms and benefits behind these proposals. The conclusion is that intelligence differences provide powerful explanations of many important real-world phenomena, and that denying their causal role requires the promotion of alternative false beliefs. Acting on these false beliefs almost invariably prevents the effective management of societal problems while creating new ones. The proper questions to ask are not about the nature of the research and the results it is expected to produce, but about whether prevailing value systems can turn truthful knowledge about cognitive differences into benign outcomes, whatever the truth may be. These value systems are the proper focus of action. Therefore, the proposal to suppress knowledge about cognitive ability differences must be based on the argument that people in modern societies will apply such knowledge in malicious rather than beneficial ways, either because of universal limitations of human nature or because of specific features of modern societies. Full article