Constructing targeted minimum loss/maximum likelihood estimators: a simple illustration to build intuition
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912484597694464 |
|---|---|
| author | Ross, Rachael K. Montoya, Lina M. Goin, Dana E. Diaz, Ivan Renson, Audrey |
| author_facet | Ross, Rachael K. Montoya, Lina M. Goin, Dana E. Diaz, Ivan Renson, Audrey |
| contents | Use of machine learning to estimate nuisance functions (e.g. outcomes models, propensity score models) in estimators used in causal inference is increasingly common, as it can mitigate bias due to model misspecification. However, it can be challenging to achieve valid inference (e.g., estimate valid confidence intervals). The efficient influence function (EIF) provides a recipe to go from a statistical estimand relevant to our causal question, to an estimator that can validly incorporate machine learning. Our companion paper, Renson et al. 2025 (arXiv:2502.05363), provides a thorough but approachable description of the EIF, along with a guide through the steps to go from a unique statistical estimand to development of one type of EIF-based estimator, the so-called one-step estimator. Another commonly used estimator based on the EIF is the targeted maximum likelihood/minimum loss estimator (TMLE). Construction of TMLEs is well-discussed in the statistical literature, but there remains a gap in translation to a more applied audience. In this letter, which supplements Renson et al., we provide a more accessible illustration of how to construct a TMLE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11680 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Constructing targeted minimum loss/maximum likelihood estimators: a simple illustration to build intuition Ross, Rachael K. Montoya, Lina M. Goin, Dana E. Diaz, Ivan Renson, Audrey Methodology Use of machine learning to estimate nuisance functions (e.g. outcomes models, propensity score models) in estimators used in causal inference is increasingly common, as it can mitigate bias due to model misspecification. However, it can be challenging to achieve valid inference (e.g., estimate valid confidence intervals). The efficient influence function (EIF) provides a recipe to go from a statistical estimand relevant to our causal question, to an estimator that can validly incorporate machine learning. Our companion paper, Renson et al. 2025 (arXiv:2502.05363), provides a thorough but approachable description of the EIF, along with a guide through the steps to go from a unique statistical estimand to development of one type of EIF-based estimator, the so-called one-step estimator. Another commonly used estimator based on the EIF is the targeted maximum likelihood/minimum loss estimator (TMLE). Construction of TMLEs is well-discussed in the statistical literature, but there remains a gap in translation to a more applied audience. In this letter, which supplements Renson et al., we provide a more accessible illustration of how to construct a TMLE. |
| title | Constructing targeted minimum loss/maximum likelihood estimators: a simple illustration to build intuition |
| topic | Methodology |
| url | https://arxiv.org/abs/2507.11680 |