A Semi-Analytical Inference Framework for Structural Causal Models

Key points

• Existing frameworks either transform SCMs into alternative tractable representations or rely on approximate inference with expressive generative models.1
• We instead perform inference using a message passing scheme directly on the SCM by representing structural mechanisms and probability densities in a common finite-dimensional function space.1
• Forward propagation combines a numerical realization of the Law of the Unconscious Statistician, while propagated densities are recovered by spline projection from LOTUS moments.1
• The same representation naturally yields backward message passing for conditioning, providing a unified framework for causal inference.1

Sources (1)

• [1]A Semi-Analytical Inference Framework for Structural Causal Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 12:17 PM

  Structural Causal Models (SCMs) provide a principled framework for observational, interventional, and counterfactual reasoning, all of which rely on probabilistic inference.

  Existing frameworks either transform SCMs into alternative tractable representations or rely on approximate inference with expressive generative models.

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