Pseudo-random Number Generator Influences on Average Treatment Effect Estimates Obtained with Machine Learning.

ABSTRACT:

Background

The use of machine learning to estimate exposure effects introduces a dependence between the results of an empirical study and the value of the seed used to fix the pseudo-random number generator.

Methods

We used data from 10,038 pregnant women and a 10% subsample (N = 1004) to examine the extent to which the risk difference for the relation between fruit and vegetable consumption and preeclampsia risk changes under different seed values. We fit an augmented inverse probability weighted estimator with two Super Learner algorithms: a simple algorithm including random forests and single-layer neural networks and a more complex algorithm with a mix of tree-based, regression-based, penalized, and simple algorithms. We evaluated the distributions of risk differences, s

SUBMITTER: Naimi AI 

PROVIDER: S-EPMC11560583 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature