BaMANI: Bayesian Multi-Algorithm causal Network Inference

TL;DR

BaMANI is an ensemble Bayesian network inference method that combines multiple algorithms to improve the reliability and robustness of causal network predictions, demonstrated in breast cancer research.

Contribution

The paper introduces BaMANI, a novel software tool that employs a multi-algorithm ensemble approach for Bayesian causal network inference, reducing algorithm-specific biases.

Findings

Ensemble approach improves causal network inference reliability.

BaMANI effectively applied to breast cancer data.

Theoretical foundation established for multi-algorithm Bayesian inference.

Abstract

Improved computational power has enabled different disciplines to predict causal relationships among modeled variables using Bayesian network inference. While many alternative algorithms have been proposed to improve the efficiency and reliability of network prediction, the predicted causal networks reflect the generative process but also bear an opaque imprint of the specific computational algorithm used. Following a ``wisdom of the crowds" strategy, we developed an ensemble learning approach to marginalize the impact of a single algorithm on Bayesian causal network inference. To introduce the approach, we first present the theoretical foundation of this framework. Next, we present a comprehensive implementation of the framework in terms of a new software tool called BaMANI (Bayesian Multi-Algorithm causal Network Inference). Finally, we describe a BaMANI use-case from biology, particularly within human breast cancer studies.