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arXiv AI By Tatiana Boura, Robert Peharz

A Semi-Analytical Inference Framework for Structural Causal Models

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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.

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