Learning Algorithms for Dynamic Treatment Regimes
Description
Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by potentially time-varying patient features and intermediate outcomes observed in previous stages. There are 3 main type methods, O-learning, Q-learning and P-learning to learn the optimal Dynamic Treatment Regimes with continuous variables. This package provide these state of arts algorithms to learn DTRs.