  Understanding causal narratives communicated in clinical notes can help make
strides towards personalized healthcare. Extracted causal information from
clinical notes can be combined with structured EHR data such as patients'
demographics, diagnoses, and medications. This will enhance healthcare
providers' ability to identify aspects of a patient's story communicated in the
clinical notes and help make more informed decisions.
  In this work, we propose annotation guidelines, develop an annotated corpus
and provide baseline scores to identify types and direction of causal relations
between a pair of biomedical concepts in clinical notes; communicated
implicitly or explicitly, identified either in a single sentence or across
multiple sentences.
  We annotate a total of 2714 de-identified examples sampled from the 2018 n2c2
shared task dataset and train four different language model based
architectures. Annotation based on our guidelines achieved a high
inter-annotator agreement i.e. Fleiss' kappa ($\kappa$) score of 0.72, and our
model for identification of causal relations achieved a macro F1 score of 0.56
on the test data. The high inter-annotator agreement for clinical text shows
the quality of our annotation guidelines while the provided baseline F1 score
sets the direction for future research towards understanding narratives in
clinical texts.
