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Model averaged double robust estimation.

Matthew Cefalu1, Francesca Dominici2, Nils Arvold3

  • 1RAND Corporation, Santa Monica, CA, USA.

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|November 29, 2016
PubMed
Summary
This summary is machine-generated.

Model averaging addresses challenges in estimating causal effects with many confounders. New model-averaged double robust (MA-DR) estimators reduce mean squared error, especially with large confounder sets.

Keywords:
causal inferenceconfoundingdouble robustnessmodel averagingpropensity scorevariable selection

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Area of Science:

  • Causal inference
  • Statistical modeling
  • Epidemiology

Background:

  • Estimating causal effects with high-dimensional confounders presents challenges.
  • Standard methods often ignore uncertainty in propensity score model selection, risking bias.

Purpose of the Study:

  • To introduce a novel model-averaged double robust (MA-DR) estimation approach.
  • To account for model uncertainty in both propensity score and outcome models.

Main Methods:

  • Developed MA-DR estimators using weighted averages of double robust estimators.
  • Evaluated MA-DR estimators through simulation studies and application to glioblastoma patient data.

Main Results:

  • MA-DR estimators maintain consistency under weaker assumptions than traditional methods.
  • Simulations showed substantial reduction in mean squared error, particularly with large confounder sets relative to sample size.

Conclusions:

  • MA-DR estimators offer a practical and generalizable solution for confounding in causal inference.
  • The approach effectively estimates causal effects, as demonstrated in a glioblastoma patient cohort.