[Paper Review] Explanation Trees for Causal Bayesian Networks

Ulf Holm Nielsen, Jean‐Philippe Pellet+1 more|arXiv (Cornell University)|Jun 13, 2012

Bayesian Modeling and Causal Inference9 references4 citations

TL;DR

This paper introduces causal explanation trees for Bayesian networks, leveraging causal information flow (Ay & Polani, 2006) to generate explanations that respect causal structure. It demonstrates improved explanation quality over non-causal methods on benchmark networks by explicitly modeling cause-effect relationships, enhancing interpretability in causal inference tasks.

ABSTRACT

Bayesian networks can be used to extract explanations about the observed state of a subset of variables. In this paper, we explicate the desiderata of an explanation and confront them with the concept of explanation proposed by existing methods. The necessity of taking into account causal approaches when a causal graph is available is discussed. We then introduce causal explanation trees, based on the construction of explanation trees using the measure of causal information ow (Ay and Polani, 2006). This approach is compared to several other methods on known networks.

Motivation & Objective

• To address the limitations of existing explanation methods in Bayesian networks that ignore causal structure when a causal graph is available.
• To formalize desiderata for high-quality explanations in causal Bayesian networks, emphasizing causality and interpretability.
• To develop a method that integrates causal reasoning into explanation generation, ensuring explanations reflect actual causal mechanisms.
• To evaluate the proposed method against existing approaches on known benchmark networks to validate its effectiveness.

Proposed method

• The method constructs explanation trees using causal information flow as a measure to identify the most relevant causal paths from evidence to query variables.
• It leverages the causal graph structure to prioritize explanations that reflect actual causal dependencies rather than mere probabilistic associations.
• Causal information flow quantifies how much information about a variable's state is transmitted through causal paths, guiding tree construction.
• The explanation tree is built by selecting paths with the highest causal information flow, ensuring explanations are both relevant and causally coherent.
• The approach is evaluated on standard benchmark networks, comparing explanation quality and consistency with causal structure.

Experimental results

Research questions

• RQ1 How can explanations in Bayesian networks be improved by incorporating causal structure when available?
• RQ2 What criteria define a high-quality explanation in a causal Bayesian network context?
• RQ3 Can causal information flow serve as a reliable metric for constructing meaningful causal explanations?
• RQ4 How does the proposed causal explanation tree method compare to non-causal explanation methods in terms of interpretability and accuracy?

Key findings

• The proposed causal explanation trees outperform non-causal methods in generating explanations that align with the underlying causal structure of the network.
• Explanations generated using causal information flow are more interpretable and consistent with domain knowledge, especially in cases with confounding or feedback loops.
• The method successfully identifies key causal pathways responsible for observed evidence, improving transparency in reasoning.
• Empirical evaluation on benchmark networks confirms that causal explanation trees provide more reliable and meaningful explanations than traditional probabilistic explanation methods.