Asymptotic Theory for Doubly Robust Estimators with Continuous-Time Nuisance Parameters

TL;DR

This paper develops a comprehensive asymptotic theory for doubly robust estimators involving continuous-time nuisance parameters, addressing a significant gap in statistical methodology for complex stochastic models.

Contribution

It introduces a general asymptotic framework for doubly robust estimators with continuous-time nuisance parameters, applicable to both model-based and machine learning approaches.

Findings

Establishes conditions for consistency and asymptotic normality.

Differentiates continuous-time setting from classical theory.

Supports flexible machine learning methods for nuisance estimation.

Abstract

Doubly robust estimators have gained widespread popularity in various fields due to their ability to provide unbiased estimates under model misspecification. However, the asymptotic theory for doubly robust estimators with continuous-time nuisance parameters remains largely unexplored. In this short communication, we address this gap by developing a general asymptotic theory for a class of doubly robust estimating equations involving stochastic processes and Riemann-Stieltjes integrals. We introduce generic assumptions on the nuisance parameter estimators that ensure the consistency and asymptotic normality of the resulting doubly robust estimator. Our results cover both the model doubly robust estimator, which relies on parametric or semiparametric models, and the rate doubly robust estimator, which allows for flexible machine learning methods. We discuss the implications of our findings and highlight the key differences between the continuous-time setting and the classical theory for doubly robust estimators. Our work provides a solid theoretical foundation for the use of doubly robust estimators in complex settings with continuous-time nuisance parameters, paving the way for future research and applications.