# Structure and Parameter Learning for Causal Independence and Causal Interaction Models

arXiv: https://arxiv.org/abs/1302.1561 · HTML: https://mlsanity.com/paper/1302.1561/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2013.

## Built on by (most cited first, 2 of 2)

- [Surjective Independence of Causal Influences for Local Bayesian Network Structures](https://mlsanity.com/paper/2509.24759/) (2025)
- [Structural Refinement of Bayesian Networks for Efficient Model Parameterisation](https://mlsanity.com/paper/2510.00334/) (2025)

## Abstract (arXiv)

This paper discusses causal independence models and a generalization of these models called causal interaction models. Causal interaction models are models that have independent mechanisms where a mechanism can have several causes. In addition to introducing several particular types of causal interaction models, we show how we can apply the Bayesian approach to learning causal interaction models obtaining approximate posterior distributions for the models and obtain MAP and ML estimates for the parameters. We illustrate the approach with a simulation study of learning model posteriors.