Causal Structure Learning: a Bayesian approach based on random graphs

TL;DR

This paper introduces a Bayesian method for learning causal structures using random graphs, demonstrating its effectiveness across different scenarios and causal complexities.

Contribution

The paper presents a novel Bayesian approach leveraging random graphs to model and learn causal relationships from data.

Findings

Successfully learned causal structures in multiple scenarios

Demonstrated the method's ability to identify optimal actions

Effective across varying task sizes and causal complexities

Abstract

A Random Graph is a random object which take its values in the space of graphs. We take advantage of the expressibility of graphs in order to model the uncertainty about the existence of causal relationships within a given set of variables. We adopt a Bayesian point of view in order to capture a causal structure via interaction and learning with a causal environment. We test our method over two different scenarios, and the experiments mainly confirm that our technique can learn a causal structure. Furthermore, the experiments and results presented for the first test scenario demonstrate the usefulness of our method to learn a causal structure as well as the optimal action. On the other hand the second experiment, shows that our proposal manages to learn the underlying causal structure of several tasks with different sizes and different causal structures.