2019/08/14 by Mark J. van der Laan, van der Laan, Mark J., David Benkeser +3 · 1 citation
Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.1908.05607
openalex publication_date 2019/08/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 L1-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.