Work

Constructing targeted minimum loss/maximum likelihood estimators: a simple illustration to build intuition

Year: 2025

Type: article

Abstract: Machine learning is increasingly used to estimate nuisance functions in causal inference. The efficient influence function (EIF) offers a principled way to construct estimators that can incorporate machine learning with valid inference (eg, estimate valid conference intervals). In this tutorial, we illustrate how to construct targeted maximum likelihood/minimum loss estimators from the EIF, a topic that is well covered in statistical literature but remains less accessible to applied researchers. A companion paper, Renson et al. 2025 (AJE, kwaf169) provides a thorough, but approachable description of the EIF and its derivation for a statistical estimand.

Source: American Journal of Epidemiology

DOI: 10.1093/aje/kwaf261

Authors: Rachael K Ross, Lina M Montoya, Dana E Goin, Iván Díaz, Audrey Renson

Institutions: University of Utah, Columbia University, University of North Carolina at Chapel Hill, New York University

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute of Environmental Health Sciences

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