arXiv:2603. 01168v3 Announce Type: replace-cross Abstract: Reliable decision-making in complex multi-agent systems requires calibrated predictions and interpretable uncertainty.
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.
arXiv:2608. 07214v1 Announce Type: cross Abstract: Modern AI is no longer a single model but an ecosystem: classical ML predictors, deep and multimodal models, large language models, and agents, each trained and tuned over different data sources and each producing outputs at scale that become inputs to the others.
The paper reviews modern causal discovery techniques that go beyond conditional independence, highlighting how distributional properties, such as asymmetries, conditional moments, and environmental variation, can identify causal structure. It distinguishes between identification sources, statistical estimators, and computational implementations, and discusses challenges in the presence of latent confounding, feedback, and cyclic models. The authors also outline consistency, uncertainty quantification, and algorithmic guarantees, and provide guidance for comparing methods and a research agenda for future work.
arXiv:2609.36985v1 Announce Type: cross Abstract: The central challenge of world modeling is to learn representations that capture how the world evolves. However, existing world models predominantly...
arXiv:2606. 19594v1 Announce Type: new Abstract: Causal abstractions formalize when a high-level structural causal model (SCM) captures the interventional behavior of a lower-level SCM.
World Models (WM) are increasingly seen as a foundation for intelligent agents that can predict, plan, and act beyond their training distribution. In this paper, we study WMs from a causal perspective across multiple levels of abstraction, ranging from perceptual observations to building a conceptual representation of the structure governing the environment dynamics.
arXiv:2606. 06440v1 Announce Type: new Abstract: Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science.