Before revision:  Our method  allows the use of arbitrary machine learning methods to control for the high dimensional state  , subject to a mean square error guarantee, while still allowing parametric estimation and construction of confidence intervals for the dynamic treatment effect parameters of interest.
After revision:  Our method applies to a general class of non-linear dynamic treatment models known as Structural Nested Mean Models and allows the use of arbitrary machine learning methods to control for the high dimensional state , subject to a mean square error guarantee, while still allowing parametric estimation and construction of confidence intervals for the dynamic treatment effect parameters of interest.