  Counterfactual inference is a useful tool for comparing outcomes of
interventions on complex systems. It requires us to represent the system in
form of a structural causal model, complete with a causal diagram,
probabilistic assumptions on exogenous variables, and functional assignments.
Specifying such models can be extremely difficult in practice. The process
requires substantial domain expertise, and does not scale easily to large
systems, multiple systems, or novel system modifications. At the same time,
many application domains, such as molecular biology, are rich in structured
causal knowledge that is qualitative in nature. This manuscript proposes a
general approach for querying a causal biological knowledge graph, and
converting the qualitative result into a quantitative structural causal model
that can learn from data to answer the question. We demonstrate the
feasibility, accuracy and versatility of this approach using two case studies
in systems biology. The first demonstrates the appropriateness of the
underlying assumptions and the accuracy of the results. The second demonstrates
the versatility of the approach by querying a knowledge base for the molecular
determinants of a severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2)-induced cytokine storm, and performing counterfactual inference to
estimate the causal effect of medical countermeasures for severely ill
patients.
