Automatic Debiased Machine Learning with Generic Machine Learning for Static and Dynamic Causal Parameters


Econometrics Seminar

United States

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Abstract: We propose a method for automated debiased machine learning in static and dynamic treatment regimes, that allows for generic machine learning estimators to be used for automatically constructing the debiasing term. As a special case, 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 propose a recursive Riesz representer M-estimation learning algorithm that estimates de-biasing corrections without the need to characterize how the correction terms look like. 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 finite sample high probability mean squared error bounds for our recursive Riesz estimation process via the use of localized Rademacher complexities.


Vasilis Syrgkanis

Vasilis Syrgkanis

Stanford University