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We extend the idea of automated debiased machine learning to the dynamictreatment regime and more generally to nested functionals. We show that themultiply robust formula for the dynamic treatment regime with discretetreatments can be re-stated in terms of a recursive Riesz representercharacterization of nested mean regressions. We then apply a recursive Rieszrepresenter estimation learning algorithm that estimates de-biasing correctionswithout the need to characterize how the correction terms look like, such asfor instance, products of inverse probability weighting terms, as is done inprior work on doubly robust estimation in the dynamic regime. Our approachdefines a sequence of loss minimization problems, whose minimizers are themulitpliers of the de-biasing correction, hence circumventing the need forsolving auxiliary propensity models and directly optimizing for the meansquared error of the target de-biasing correction. We provide furtherapplications of our approach to estimation of dynamic discrete choice modelsand estimation of long-term effects with surrogates.

Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals