  Gas turbine superalloys experience hot corrosion, driven by factors including
corrosive deposit flux, temperature, gas composition, and component material.
The full mechanism still needs clarification and research often focuses on
laboratory work. As such, there is interest in causal discovery to confirm the
significance of factors and identify potential missing causal relationships or
co-dependencies between these factors. The causal discovery algorithm Fast
Causal Inference (FCI) has been trialled on a small set of laboratory data,
with the outputs evaluated for their significance to corrosion propagation, and
compared to existing mechanistic understanding. FCI identified the salt
deposition flux as the most influential corrosion variable for this limited
dataset. However, HCl was the second most influential for pitting regions,
compared to temperature for more uniformly corroding regions. Thus FCI
generated causal links aligned with literature from a randomised corrosion
dataset, while also identifying the presence of two different degradation modes
in operation.
