Is it possible to explain sklearn isolation forest prediction?
You are creating an ensemble of trees so and the path of a given instance will be different for each tree in the ensemble. To detect an anomaly the isolation forest takes the average path length (number of splits to isolate a sample) of all the trees for a given instance and uses this to determine if it is an anomaly (average shorter path lengths indicate anomalies). As you are looking at the average of a set of trees there is no 'exact' path.
Within my knowledge, your best bet would be to use something like SHAP, as you mentioned, but you could also train only a few estimators and look at the path taken for a given instance in these trees to get an insight into the decisions.
I'm using the isolation forest algorithm from sklearn to do some unsupervised anomaly detection. 
I need to explained the predictions and I was wondering if there is any way to get the paths that lead to the decision for each sample.
I usually used SHAP or ELI5 but i'd like to do something more custom. So i need the exact path.