Given a set of options where one option is selected prior to an outcome, how to model optimal selection that will increase likelihood of (+) outcome

Say that we have a set of treatment plans (the options) available to a patient. Treatment plans can be invasive-surgery, no-surgery, less-invasive surgery ext...

We have a dataset where a treatment plan was chosen for a patient and and we also have their outcome (Survived/Did-not-survive).

What is the best way to go about grading/ranking/choosing an optimal treatment plan so that we retain optimal survival rates.

To me this sounds like a recommendation algorithm but the way it seems most recommendation algorithm work is by modelling best recommendation/option based off of whether that option was selected. Here the outcome is not whether a particular treatment/recommendation was selected (non-selected treatment may have been better) but instead whether a patient survived after the selected treatment. Given the fact that we don't have outcomes for the options that were not selected and only have outcomes for the treatment that was selected - How can I best create a model that produces a rank/grade of optimal treatment plan that increases likelihood of survival? Example dataset below