  Child mortality from preventable diseases such as pneumonia and diarrhoea in
low and middle-income countries remains a serious global challenge. We combine
knowledge with available Demographic and Health Survey (DHS) data from India,
to construct Causal Bayesian Networks (CBNs) and investigate the factors
associated with childhood diarrhoea. We make use of freeware tools to learn the
graphical structure of the DHS data with score-based, constraint-based, and
hybrid structure learning algorithms. We investigate the effect of missing
values, sample size, and knowledge-based constraints on each of the structure
learning algorithms and assess their accuracy with multiple scoring functions.
Weaknesses in the survey methodology and data available, as well as the
variability in the CBNs generated by the different algorithms, mean that it is
not possible to learn a definitive CBN from data. However, knowledge-based
constraints are found to be useful in reducing the variation in the graphs
produced by the different algorithms, and produce graphs which are more
reflective of the likely influential relationships in the data. Furthermore,
valuable insights are gained into the performance and characteristics of the
structure learning algorithms. Two score-based algorithms in particular, TABU
and FGES, demonstrate many desirable qualities; a) with sufficient data, they
produce a graph which is similar to the reference graph, b) they are relatively
insensitive to missing values, and c) behave well with knowledge-based
constraints. The results provide a basis for further investigation of the DHS
data and for a deeper understanding of the behaviour of the structure learning
algorithms when applied to real-world settings.
