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About this paper
Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas
Researchers at IDSIA demonstrated an exact correspondence between Structural Causal Models and credal networks, providing a systematic numerical framework for causal inference. This approach precisely computes causal effects and delivers tight, exact bounds for unidentifiable queries by leveraging existing credal network inference algorithms.
Key insights
- Observed empirical distributions of endogenous variables in an SCM induce a set of linear constraints on the probabilities of the latent exogenous variables, which define a credal set.
- Credal networks inherently produce interval-valued probabilities, providing tight, exact bounds for unidentifiable causal queries and collapsing to precise point values for identifiable ones.
- The systematic mapping allows leveraging mature numerical algorithms developed for credal networks to solve complex causal inference problems, offering an automated alternative to symbolic causal calculi.
Summary by alphaXiv.
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