  Learning causal relationships solely from observational data often fails to
reveal the underlying causal mechanisms due to the vast search space of
possible causal graphs, which can grow exponentially, especially for greedy
algorithms using score-based approaches. Leveraging prior causal information,
such as the presence or absence of causal edges, can help restrict and guide
the score-based discovery process, leading to a more accurate search. In the
healthcare domain, prior knowledge is abundant from sources like medical
journals, electronic health records (EHRs), and clinical intervention outcomes.
This study introduces a knowledge-guided causal structure search (KGS) approach
that utilizes observational data and structural priors (such as causal edges)
as constraints to learn the causal graph. KGS leverages prior edge information
between variables, including the presence of a directed edge, the absence of an
edge, and the presence of an undirected edge. We extensively evaluate KGS in
multiple settings using synthetic and benchmark real-world datasets, as well as
in a real-life healthcare application related to oxygen therapy treatment. To
obtain causal priors, we use GPT-4 to retrieve relevant literature information.
Our results show that structural priors of any type and amount enhance the
search process, improving performance and optimizing causal discovery. This
guided strategy ensures that the discovered edges align with established causal
knowledge, enhancing the trustworthiness of findings while expediting the
search process. It also enables a more focused exploration of causal
mechanisms, potentially leading to more effective and personalized healthcare
solutions.
