TL;DR
The effectiveness of the proposed counterfactual and intervention mechanisms for in-domain multi-modal rumor detection, providing methodological support for trustworthy multi-modal information understanding systems is demonstrated.
An Attention-level Causal Intervention Framework (ACIM), which performs causal adjustment directly within the attention learning process to mitigate confounding effects and estimate attention-level intervention effects without requiring fully observed confounders.
Multi-touch attribution (MTA) aims to estimate the causal effect of advertising touchpoints on user conversions, providing essential support for budget allocation and advertising recommendation. Existing causal MTA methods often struggle to capture complex temporal dependencies in long touchpoint sequences and fail to...
Self-supervised Causal Effects Estimation is proposed, a novel framework that integrates causal priors with self-supervised learning to construct balanced and predictive representations for causal effects estimation that consistently outperforms state-of-the-art methods.
Modern explainable AI (XAI) methods are widely used to audit deep neural networks, yet a growing body of evidence shows that the very explanations they produce can be hijacked by spurious correlations - statistical shortcuts between non-causal input features (image background, demographic attributes, textual artefacts)...
Sequential RecSys are central to modern personalization, exploiting user's historical interaction sequences to drive next-step decisions. Deep learning models, particularly CNN and Transformer-based architectures, have proven highly effective at capturing temporal dependencies in these histories. For transparency and t...
Experimental results show that the proposed method significantly reduces detection latency, requiring substantially fewer messages for prediction, while maintaining competitive or superior performance compared to state-of-the-art baselines.
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