arXiv:2511.05050v3 Announce Type: replace-cross Abstract: In this study, a scalable online kernel learning framework is proposed for estimating bidirectional causal effects in systems characterized by mutual dependence and heteroskedasticity. Traditional causal inference often focuses on unidirectional effects, overlooking the common bidirectional relationships in real-world phenomena. Building on heteroskedasticity-based identification, the proposed method integrates a quasi-maximum likelihood estimator for simultaneous equation models with large scale online kernel learning. It employs rando
The paper introduces a refined method for estimating bidirectional causal effects, addressing limitations in traditional causal inference and incorporating large-scale online kernel learning, which aligns with current advancements in AI methodology.
This development proposes a more accurate and scalable way to understand complex causal relationships in systems with mutual dependence, crucial for advanced AI agent development and robust decision-making in autonomous systems.
The ability to more effectively model bidirectional causality in large-scale online settings will improve the precision and reliability of AI agents operating in dynamic, mutually dependent environments.
- · AI researchers
- · Developers of AI agents
- · Sectors with complex interdependent systems (e.g., finance, robotics)
- · Traditional causal inference methods
- · AI systems relying solely on unidirectional causal models
Improved understanding and modeling of complex systems with mutual dependencies in AI.
Development of more sophisticated and robust AI agents capable of navigating highly interactive environments.
Enhanced predictive capabilities and reduced 'black box' issues within advanced AI, potentially accelerating trust and adoption in critical applications.
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