Structure and Parameter Learning for Causal Independence and Causal Interaction Models

TL;DR

This paper introduces causal interaction models, generalizing causal independence models, and demonstrates how Bayesian methods can be used to learn these models, with a simulation study illustrating the approach.

Contribution

It presents a new class of causal interaction models and applies Bayesian learning techniques to estimate their parameters and posteriors.

Findings

Successful application of Bayesian methods to learn causal interaction models

Approximate posterior distributions obtained for the models

Simulation study demonstrating the learning approach

Abstract

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.