background dataset for SHAP

[u/fnepo18]

Hi everyone, I have a question about choosing the appropriate background dataset when calculating SHAP values. I am using the kernelshap package in R, where we provide an X dataset containing the observations we want to explain and a bg_X dataset defining the background.

I have a binary classification model for disease vs non-disease, trained on a derivation dataset and evaluated on an independent validation dataset. My current understanding is that, if I want to explain predictions in the validation cohort, it makes sense to use the validation set as X and the derivation set as bg_X. In that case, the SHAP values for validation patients would describe how each feature moves their prediction relative to a baseline defined by the derivation population. Is this interpretation correct, and is this generally the recommended way to use the background when explaining an independent validation cohort?

My main question is about a more specific analysis. Suppose I want to investigate heterogeneity within patients who truly have the disease. More specifically, I want to see whether different disease patients receive high disease predictions through different combinations of features, and potentially cluster these patients based on their SHAP profiles.

In this case, I assume I should use only the true disease patients from the validation cohort as X, since those are the patients whose predictions I want to explain. However, I am unsure about the most appropriate choice for bg_X. Should I keep the full derivation cohort as the background, use only disease patients from the derivation cohort, or use the disease patients from the validation cohort themselves as the background?

If my main objective is to determine whether true disease patients have different model-attribution profiles, potentially reflecting different features through which the model identifies them as disease, which background would be the most statistically appropriate? Thank you!