  Structure learning algorithms that learn the graph of a Bayesian network from
observational data often do so by assuming the data correctly reflect the true
distribution of the variables. However, this assumption does not hold in the
presence of measurement error, which can lead to spurious edges. This is one of
the reasons why the synthetic performance of these algorithms often
overestimates real-world performance. This paper describes an algorithm that
can be added as an additional learning phase at the end of any structure
learning algorithm, and serves as a correction learning phase that removes
potential false positive edges. The results show that the proposed correction
algorithm successfully improves the graphical score of four well-established
structure learning algorithms spanning different classes of learning in the
presence of measurement error.
