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The graph of a Bayesian Network (BN) can be machine learned, determined bycausal knowledge, or a combination of both. In disciplines like bioinformatics,applying BN structure learning algorithms can reveal new insights that wouldotherwise remain unknown. However, these algorithms are less effective when theinput data are limited in terms of sample size, which is often the case whenworking with real data. This paper focuses on purely machine learned and purelyknowledge-based BNs and investigates their differences in terms of graphicalstructure and how well the implied statistical models explain the data. Thetests are based on four previous case studies whose BN structure was determinedby domain knowledge. Using various metrics, we compare the knowledge-basedgraphs to the machine learned graphs generated from various algorithmsimplemented in TETRAD spanning all three classes of learning. The results showthat, while the algorithms produce graphs with much higher model selectionscore, the knowledge-based graphs are more accurate predictors of variables ofinterest. Maximising score fitting is ineffective in the presence of limitedsample size because the fitting becomes increasingly distorted with limiteddata, guiding algorithms towards graphical patterns that share higher fittingscores and yet deviate considerably from the true graph. This highlights thevalue of causal knowledge in these cases, as well as the need for moreappropriate fitting scores suitable for limited data. Lastly, the experimentsalso provide new evidence that support the notion that results from simulateddata tell us little about actual real-world performance.

How do some Bayesian Network machine learned graphs compare to causal knowledge?