Before revision:  We formulate the problem as a linear state space Markov process with a high dimensional state and propose an extension of the double/debiased machine learning framework to estimate the dynamic effects of treatments  .
After revision:  We formulate the problem as a linear state space Markov process with a high dimensional state and propose an extension of the double/debiased machine learning framework to estimate the dynamic effects of treatments , which can be viewed as a Neyman orthogonal (locally robust) cross-fitted version of g-estimation in the dynamic treatment regime .