Causal discovery using dynamically requested knowledge
This work addresses the problem of improving causal discovery accuracy for practitioners in complex systems, offering an incremental advancement by enhancing how human expertise is utilized in structure learning.
The paper tackles the challenge of learning causal Bayesian network structures by introducing a method where the algorithm dynamically requests human knowledge for uncertain relationships during learning, integrated into the Tabu algorithm, resulting in considerable gains in structural accuracy compared to existing knowledge integration approaches.
Causal Bayesian Networks (CBNs) are an important tool for reasoning under uncertainty in complex real-world systems. Determining the graphical structure of a CBN remains a key challenge and is undertaken either by eliciting it from humans, using machine learning to learn it from data, or using a combination of these two approaches. In the latter case, human knowledge is generally provided to the algorithm before it starts, but here we investigate a novel approach where the structure learning algorithm itself dynamically identifies and requests knowledge for relationships that the algorithm identifies as uncertain during structure learning. We integrate this approach into the Tabu structure learning algorithm and show that it offers considerable gains in structural accuracy, which are generally larger than those offered by existing approaches for integrating knowledge. We suggest that a variant which requests only arc orientation information may be particularly useful where the practitioner has little preexisting knowledge of the causal relationships. As well as offering improved accuracy, the approach can use human expertise more effectively and contributes to making the structure learning process more transparent.