  We extend the idea of automated debiased machine learning to the dynamic
treatment regime and more generally to nested functionals. We show that the
multiply robust formula for the dynamic treatment regime with discrete
treatments can be re-stated in terms of a recursive Riesz representer
characterization of nested mean regressions. We then apply a recursive Riesz
representer estimation learning algorithm that estimates de-biasing corrections
without the need to characterize how the correction terms look like, such as
for instance, products of inverse probability weighting terms, as is done in
prior work on doubly robust estimation in the dynamic regime. Our approach
defines a sequence of loss minimization problems, whose minimizers are the
mulitpliers of the de-biasing correction, hence circumventing the need for
solving auxiliary propensity models and directly optimizing for the mean
squared error of the target de-biasing correction. We provide further
applications of our approach to estimation of dynamic discrete choice models
and estimation of long-term effects with surrogates.
