SHAP on Forest Causal Regressor

[gcasamat]

Hi,
thanks for this very useful package.
I would like to compute SHAP values of a causal forest model.
I ran a code similar to the one given in https://skgrf.readthedocs.io/en/latest/tree/tree_interface.html#shap , except that my model is :

cf = GRFForestCausalRegressor(enable_tree_details = True)
cf.fit(X, Y, W, Y_hat_nomissing, W_hat_nomissing)

This works fine. However, SHAP considers as predictions of my model the predicted values of Y, whereas I would like to consider as predictions the conditional effects of my treatment variable W on the target Y, which are typically the values given by cf.predict().
I wonder if there exists a way of achieving my desired outcome?