# Surjective Independence of Causal Influences for Local Bayesian Network Structures

arXiv: https://arxiv.org/abs/2509.24759 · HTML: https://mlsanity.com/paper/2509.24759/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 52 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [A survey of Bayesian Network structure learning](https://mlsanity.com/paper/2109.11415/) (2021)
- [A Generalization of the Noisy-Or Model](https://mlsanity.com/paper/1303.1479/) (2013)
- [Causal Independence for Knowledge Acquisition and Inference](https://mlsanity.com/paper/1303.1468/) (2013)
- [Parameter Adjustment in Bayes Networks. The generalized noisy OR-gate](https://mlsanity.com/paper/1303.1465/) (2013)
- [Structure and Parameter Learning for Causal Independence and Causal Interaction Models](https://mlsanity.com/paper/1302.1561/) (2013)
- [Practical Issues in Constructing a Bayes' Belief Network](https://mlsanity.com/paper/1304.2725/) (2013)

## Built on by (most cited first, 1 of 1)

- [Structural Refinement of Bayesian Networks for Efficient Model Parameterisation](https://mlsanity.com/paper/2510.00334/) (2025)

## Abstract (arXiv)

The very expressiveness of Bayesian networks can introduce fresh challenges due to the large number of relationships they often model. In many domains, it is thus often essential to supplement any available data with elicited expert judgements. This in turn leads to two key challenges: the cognitive burden of these judgements is often very high, and there are a very large number of judgements required to obtain a full probability model. We can mitigate both issues by introducing assumptions such as independence of causal influences (ICI) on the local structures throughout the network, restricting the parameter space of the model. However, the assumption of ICI is often unjustified and overly strong. In this paper, we introduce the surjective independence of causal influences (SICI) model which relaxes the ICI assumption and provides a more viable, practical alternative local structure model that facilitates efficient Bayesian network parameterisation.