  We consider evaluating the causal effects of dynamic treatments, i.e. of
multiple treatment sequences in various periods, based on double machine
learning to control for observed, time-varying covariates in a data-driven way
under a selection-on-observables assumption. To this end, we make use of
so-called Neyman-orthogonal score functions, which imply the robustness of
treatment effect estimation to moderate (local) misspecifications of the
dynamic outcome and treatment models. This robustness property permits
approximating outcome and treatment models by double machine learning even
under high dimensional covariates and is combined with data splitting to
prevent overfitting. In addition to effect estimation for the total population,
we consider weighted estimation that permits assessing dynamic treatment
effects in specific subgroups, e.g. among those treated in the first treatment
period. We demonstrate that the estimators are asymptotically normal and
$\sqrt{n}$-consistent under specific regularity conditions and investigate
their finite sample properties in a simulation study. Finally, we apply the
methods to the Job Corps study in order to assess different sequences of
training programs under a large set of covariates.
