Don't let your analysis go to seed: on the impact of random seed on machine learning-based causal inference.
Epidemiology
; 2024 Aug 16.
Article
en En
| MEDLINE
| ID: mdl-39150861
ABSTRACT
Machine learning techniques for causal effect estimation can enhance the reliability of epidemiologic analyses, reducing their dependence on correct model specifications. However, the stochastic nature of many machine learning algorithms implies that the results derived from such approaches may be influenced by the random seed that is set prior to model fitting. In this work, we highlight the substantial influence of random seeds on a popular approach for machine learning-based causal effect estimation, namely doubly robust estimators. We illustrate that varying seeds can yield divergent scientific interpretations of doubly robust estimates produced from the same dataset. We propose techniques for stabilizing results across random seeds and, through an extensive simulation study, demonstrate that these techniques effectively neutralize seed-related variability without compromising the statistical efficiency of the estimators. Based on these findings, we offer practical guidelines to minimize the influence of random seeds in real-world applications, and we encourage researchers to explore variability due to random seed when implementing any method that involves random steps.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Epidemiology
Asunto de la revista:
EPIDEMIOLOGIA
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Estados Unidos