Asymptotically Efficient Data-adaptive Penalized Shrinkage Estimation with Application to Causal Inference

TL;DR

This paper introduces a data-adaptive penalized estimation method that improves finite-sample mean-squared error of non-parametric estimators in causal inference, while maintaining asymptotic efficiency, demonstrated through simulations and real data application.

Contribution

It develops a novel penalized estimator with data-driven tuning that enhances finite-sample performance without sacrificing asymptotic optimality.

Findings

Penalized estimators show lower MSE than unpenalized ones in simulations.

The method maintains asymptotic efficiency of the original estimators.

Application to kidney dialysis provider quality measures demonstrates practical utility.

Abstract

A rich literature exists on constructing non-parametric estimators with optimal asymptotic properties. In addition to asymptotic guarantees, it is often of interest to design estimators with desirable finite-sample properties; such as reduced mean-squared error of a large set of parameters. We provide examples drawn from causal inference where this may be the case, such as estimating a large number of group-specific treatment effects. We show how finite-sample properties of non-parametric estimators, particularly their variance, can be improved by careful application of penalization. Given a target parameter of interest we derive a novel penalized parameter defined as the solution to an optimization problem that balances fidelity to the original parameter against a penalty term. By deriving the non-parametric efficiency bound for the penalized parameter, we are able to propose simple data-adaptive choices for the L1 and L2 tuning parameters designed to minimize finite-sample mean-squared error while preserving optimal asymptotic properties. The L1 and L2 penalization amounts to an adjustment that can be performed as a post-processing step applied to any asymptotically normal and efficient estimator. We show in extensive simulations that this adjustment yields estimators with lower MSE than the unpenalized estimators. Finally, we apply our approach to estimate provider quality measures of kidney dialysis providers within a causal inference framework.