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Area of Science:

  • Biostatistics
  • Epidemiology
  • Machine Learning

Background:

  • Growing interest in nonparametric machine learning for robust propensity score estimation.
  • Limited research on nonparametric methods for clustered data settings.

Purpose of the Study:

  • Extend nonparametric propensity score estimation to clustered data.
  • Investigate performance of various models under confounding.

Main Methods:

  • Developed a general algorithm for random effects in machine learning models (GBM).
  • Simulated clustered data with nonlinear treatment and unmeasured confounding.
  • Compared logistic regression, GBM, and BART for IPW using fixed/random effects and single-level models.

Main Results:

  • Nonparametric methods were unbiased without confounding; logistic regression showed moderate bias.
  • Fixed/random effects models significantly reduced bias with cluster-level confounding.
  • Fixed effects GBM and logistic regression performed best under confounding.

Conclusions:

  • Clustered IPW is preferable to marginal IPW.
  • Balance Super Learner outperforms standard Super Learner but not best candidate models.
  • Nonparametric methods with random/fixed effects are crucial for causal inference in clustered data.