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Area of Science:

  • Causal Inference
  • Semiparametric Statistics
  • Biostatistics

Background:

  • Doubly robust methods are used for estimating marginal causal effects.
  • Data-adaptive methods estimate nuisance parameters in these models.
  • Positivity violations can challenge the reliability of these estimates.

Purpose of the Study:

  • To evaluate the Targeted Minimum Loss-based Estimation (TMLE) procedure.
  • To assess the impact of near positivity violations on causal effect estimation.
  • To introduce a diagnostic tool for identifying bias in data-adaptive propensity score estimation.

Main Methods:

  • Simulation study using TMLE to estimate average treatment effect.
  • Comparison of parametric and data-adaptive propensity score estimation methods.
  • Adaptation of a bootstrap resampling procedure for diagnostic purposes.

Main Results:

  • Near positivity violations led to separation in propensity score densities.
  • Data-adaptive methods resulted in divergent and biased treatment effect estimates compared to parametric methods.
  • The adapted bootstrap procedure detected instability and poor coverage associated with data-adaptive methods.

Conclusions:

  • Data-adaptive propensity score estimation can introduce significant bias and poor coverage under near positivity violations.
  • The adapted bootstrap procedure is effective in diagnosing instability in causal effect estimation.
  • This diagnostic tool enhances the reliability of doubly robust semiparametric methods.