On the Relationship between Bayesian Networks and Probabilistic Structural Causal Models
In this paper, the relationship between probabilistic graphical models, in particular Bayesian networks, and causal diagrams, also called structural causal models, is studied. Structural causal models are deterministic models, based on structural equations or functions, that can be provided with uncertainty by adding independent, unobserved random variables to the models, equipped with probability distributions. One question that arises is whether a Bayesian network that has obtained from expert knowledge or learnt from data can be mapped to a probabilistic structural causal model, and whether or not this has consequences for the network structure and probability distribution. We show tha...
| Authors | Peter J. F. Lucas, Eleonora Zullo, Fabio Stella |
|---|---|
| Primary category | cs.AI |
| Published | 28 March 2026 |
| Last updated at source | 23 April 2026 |
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