arXiv:2603. 01168v3 Announce Type: replace-cross Abstract: Reliable decision-making in complex multi-agent systems requires calibrated predictions and interpretable uncertainty.
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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.
arXiv:2607. 26336v1 Announce Type: new Abstract: In model-based reinforcement learning, world models exist as internal simulators, but their training often conflates statistical correlations with causal mechanisms.
The paper introduces a semi‑analytical inference framework for Structural Causal Models (SCMs) that performs message passing directly on the SCM using a common finite‑dimensional function space. It represents structural mechanisms and probability densities with tensor‑product B‑spline bases, enabling forward propagation via a numerical Law of the Unconscious Statistician and backward conditioning through spline projection. Experiments on randomly generated continuous SCMs show that the method accurately approximates Monte Carlo solutions and improves with higher basis resolution.
arXiv:2607. 01936v1 Announce Type: cross Abstract: Learning causal models from high-dimensional data is a significant challenge, particularly in real-world settings where violations of core assumptions lead to causal identifiability issues.
Structural Causal Models (SCMs) provide a principled framework for observational, interventional, and counterfactual reasoning, all of which rely on probabilistic inference. Existing frameworks either...
arXiv:2606. 10607v1 Announce Type: cross Abstract: Causal discovery aims to uncover causal structures from observational data, which is crucial for real-world decision-making.