BaMANI: Bayesian Multi-Algorithm causal Network Inference
This work addresses the need for more robust causal network predictions in fields like biology, though it is incremental as it builds on existing ensemble strategies.
The authors tackled the problem of algorithm-specific biases in Bayesian causal network inference by developing an ensemble learning approach, BaMANI, which marginalizes the impact of individual algorithms to improve reliability, as demonstrated in a human breast cancer study.
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.