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Optimizing Variance-Bias Trade-Off in the TWANG Package for Estimation of Propensity Scores

Author

Listed:
  • Layla Parast
  • Daniel F. McCaffrey
  • Lane F. Burgette
  • Fernando Hoces de la Guardia
  • Daniela Golinelli
  • Jeremy N. V. Miles
  • Beth Ann Griffin

Abstract

While propensity score weighting has been shown to reduce bias in treatment effect estimation when selection bias is present, it has also been shown that such weighting can perform poorly if the estimated propensity score weights are highly variable.

Suggested Citation

  • Layla Parast & Daniel F. McCaffrey & Lane F. Burgette & Fernando Hoces de la Guardia & Daniela Golinelli & Jeremy N. V. Miles & Beth Ann Griffin, "undated". "Optimizing Variance-Bias Trade-Off in the TWANG Package for Estimation of Propensity Scores," Mathematica Policy Research Reports 5881d9ed1776448a91d9ebbf9, Mathematica Policy Research.
    • Handle: RePEc:mpr:mprres:5881d9ed1776448a91d9ebbf9de579af
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    File URL: https://rd.springer.com/article/10.1007/s10742-016-0168-2
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    Keywords

    Causal inference ; Propensity score ; Machine learning;
    All these keywords.

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