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

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 (e.g., estimate valid conference intervals). In this Tutorial, we illustrate how to construct targeted maximum likelihood/minimum loss estimators (TMLE) 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.

SUBMITTER: Ross RK 

PROVIDER: S-EPMC12643179 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature