arXiv:2512.19510v2 Announce Type: replace Abstract: Conditional independence (CI) is central to causal inference, feature selection, and graphical modeling, yet it is untestable in many settings without additional assumptions. Existing CI tests often rely on restrictive structural conditions, limiting their validity. Kernel methods using partial covariance operators offer a more principled approach but suffer from limited adaptivity and scalability. In this work, we explore whether representation learning can help address these limitations. Specifically, we focus on representations derived fro
The increasing complexity of AI models and the critical reliance on causal inference across various domains necessitate more robust and scalable methods for conditional independence testing.
Improved conditional independence testing enhances the validity and scalability of AI systems, particularly in critical areas like causal inference, which underpins explainable AI and responsible decision-making.
The development of more adaptable and scalable CI tests through representation learning could address current limitations in AI's ability to discern true causal relationships from mere correlations.
- · AI researchers and developers
- · Causal inference practitioners
- · Industries relying on explainable AI
- · Methods relying on restrictive CI test assumptions
More accurate and scalable causal models become feasible across various AI applications.
This could accelerate progress in autonomous AI agents and complex decision-making systems that require reliable causal understanding.
Broader adoption of truly causal AI could lead to more robust and less biased automated systems, impacting areas from finance to healthcare.
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