A Riesz Representer Perspective on Targeted Learning

TL;DR

This paper introduces a unified estimation approach for complex causal inference problems using Riesz representers, simplifying the derivation of estimators for various statistical estimands.

Contribution

It develops a targeted minimum loss-based estimation method leveraging Riesz representers for nested linear functionals, unifying efficient estimation across multiple causal inference estimands.

Findings

The method efficiently estimates effects of time-varying treatments.

Reduces mathematical complexity in estimator derivation.

Validated through numerical experiments and real data re-analysis.

Abstract

As research in causal inference has sought to address more complex scientific questions, the number of specialized estimands in the field has proliferated. Recognition that many of these estimands share a common linear form has generated interest in simplifying estimation procedures using Riesz representers. In this work, we construct a targeted minimum loss-based estimation procedure for nested linear functionals, leveraging Riesz representers of a general recursive form. The proposed method unifies asymptotically efficient estimation for a variety of statistical estimands that originate in causal inference, including the effects of time-varying treatments under treatment-confounder feedback and direct and indirect effects from causal mediation analysis. We demonstrate how our proposal reduces the need for laborious and technically challenging mathematical derivations when constructing estimators of common statistical estimands under complex forms of censoring and sampling. We investigate and validate the properties of the proposed procedures in numerical experiments, discuss open-source software facilitating their implementation, and illustrate their application in a re-analysis of data from an HIV vaccine efficacy trial.