arXiv:2603.02159v2 Announce Type: replace-cross Abstract: Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding. Despite substantial methodological advances, existing approaches rarely provide reliable epistemic uncertainty (EU) quantification. We address this gap through a Deconditional Gaussian Process (DGP) framework for uncertainty-aware causal learning. Our formulation recovers popular kernel estimators as the posterior mean, ensuring predictive precision, while the posterior variance y
The increasing complexity and opacity of AI models necessitate more robust causal inference methods, especially when dealing with unobserved confounding variables in real-world applications.
Reliable uncertainty quantification in causal inference is critical for deploying AI systems in high-stakes environments, enabling better decision-making and trust in AI-driven insights.
The proposed Deconditional Gaussian Process framework offers a way to embed uncertainty quantification directly into causal learning, moving beyond point estimates towards more robust and interpretable causal models.
- · AI researchers and data scientists
- · Industries relying on causal inference (e.g., healthcare, finance, policy)
- · Developers of robust AI systems
- · Developers of AI systems lacking uncertainty quantification
- · Methods providing only point estimates for causal effects
Improved reliability and transparency of AI systems making causal claims.
Faster adoption of AI in regulated industries where understanding causal links and their uncertainty is paramount.
New standards and regulations emerging for uncertainty quantification in AI predictions and causal inference.
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Read at arXiv cs.LG