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

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Area of Science:

  • Epidemiology
  • Machine Learning in Healthcare
  • Statistical Modeling

Background:

  • Machine learning (ML) for exposure effect estimation creates a dependency between study outcomes and pseudo-random number generator (PRNG) seed values.
  • This seed dependence introduces potential variability into empirical research findings.

Purpose of the Study:

  • To assess the impact of different PRNG seed values on risk difference estimates.
  • To examine the variability in risk differences for the association between fruit/vegetable consumption and preeclampsia risk.

Main Methods:

  • Utilized data from 10,038 pregnant women and a 10% subsample (N=1004).
  • Employed an augmented inverse probability weighted estimator with two Super Learner algorithms (simple and complex).
  • Evaluated risk differences, standard errors, and P values across 5000 different seed values.

Main Results:

  • Significant variability in risk difference estimates was observed, influenced by the stacking algorithm.
  • The interquartile range width for risk differences varied notably between algorithms and sample sizes.
  • Medians of risk difference distributions differed based on sample size and algorithm complexity.

Conclusions:

  • Findings highlight concerns regarding "p-hacking" and the need for advanced evidentiary thresholds in empirical research.
  • Results dependent on PRNG seed values necessitate careful interpretation.
  • Emphasizes the importance of transparency and reproducibility in ML-driven epidemiological studies.