Efficient Estimation of Pathwise Differentiable Target Parameters with the Undersmoothed Highly Adaptive Lasso
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We consider estimation of a functional parameter of a realistically modeled data distribution based on observing independent and identically distributed observations. We define an m-th order Spline Highly Adaptive Lasso Minimum Loss Estimator (Spline HAL-MLE) of a functional parameter that is defined by minimizing the empirical risk function over an m-th order smoothness class of functions. We show that this m-th order smoothness class consists of all functions that can be represented as an infinitesimal linear combination of tensor products of ≤ m-th order spline-basis functions, and involves assuming m-derivatives in each coordinate. By selecting m with cross-validation we obtain a Spline-HAL-MLE that is able to adapt to the underlying unknown smoothness of the true function, while guaranteeing a rate of convergence faster than n^-1/4, as long as the true function is cadlag (right-continuous with left-hand limits) and has finite sectional variation norm. The m=0-smoothness class consists of all cadlag functions with finite sectional variation norm and corresponds with the original HAL-MLE defined in van der Laan (2015). In this article we establish that this Spline-HAL-MLE yields an asymptotically efficient estimator of any smooth feature of the functional parameter under an easily verifiable global undersmoothing condition. A sufficient condition for the latter condition is that the minimum of the empirical mean of the selected basis functions is smaller than a constant times n^-1/2, which is not parameter specific and enforces the selection of the L_1-norm in the lasso to be large enough to include sparsely supported basis. We demonstrate our general result for the m=0-HAL-MLE of the average treatment effect and of the integral of the square of the data density. We also present simulations for these two examples confirming the theory.
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