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.

翻译：本文将自动去偏机器学习的思想拓展至动态处理机制及更一般的嵌套泛函。我们证明，离散处理场景下动态处理机制的多重鲁棒公式可基于嵌套均值回归的递归Riesz表示特征重新表述。进而采用递归Riesz表示估计学习算法，该算法无需刻画校正项的具体形式（例如以往动态机制双重稳健估计研究中使用的逆概率加权项乘积），即可估计去偏校正。该方法定义了一组损失最小化问题，其最小化即为去偏校正的乘子，从而规避了求解辅助倾向性模型的需求，直接优化目标去偏校正的均方误差。我们进一步将该方法应用于动态离散选择模型估计以及基于替代指标的长期效应估计。