  Structural causal models (SCMs) provide a principled approach to identifying
causation from observational and experimental data in disciplines ranging from
economics to medicine. However, SCMs, which is typically represented as
graphical models, cannot rely only on data, rather require support of domain
knowledge. A key challenge in this context is the absence of a methodological
framework for encoding priors (background knowledge) into causal models in a
systematic manner. We propose an abstraction called causal knowledge hierarchy
(CKH) for encoding priors into causal models. Our approach is based on the
foundation of "levels of evidence" in medicine, with a focus on confidence in
causal information. Using CKH, we present a methodological framework for
encoding causal priors from various information sources and combining them to
derive an SCM. We evaluate our approach on a simulated dataset and demonstrate
overall performance compared to the ground truth causal model with sensitivity
analysis.
