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Complier stochastic direct effects: identification and robust estimation.

Kara E Rudolph1, Oleg Sofrygin2, Mark J van der Laan2

  • 1Department of Epidemiology, Columbia University, New York, New York.

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|September 17, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces the complier stochastic direct effect (CSDE) to understand direct exposure-outcome links. New methods like EE and TMLE offer more reliable and efficient estimation than IPTW, reducing bias in mediation analysis.

Keywords:
Mediationinstrumental variablestargeted minimum loss-based estimation

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Area of Science:

  • Causal inference
  • Biostatistics
  • Epidemiology

Background:

  • Mediation analysis is crucial for elucidating exposure-outcome mechanisms.
  • Understanding direct effects, independent of mediators, is essential.
  • Existing methods may not adequately capture direct causal pathways.

Purpose of the Study:

  • Introduce and define the complier stochastic direct effect (CSDE) as a novel estimand.
  • Develop and evaluate estimators for the CSDE.
  • Compare the performance of different estimation methods.

Main Methods:

  • Proposed several estimators for CSDE: ratio of IPTW, EE, and TMLE.
  • Introduced a direct TMLE targeting the CSDE.
  • Evaluated estimators using simulation and the Moving to Opportunity experiment.

Main Results:

  • IPTW estimator showed significant finite sample bias (>40% with N=100).
  • EE and direct CSDE-targeting TMLE estimators were less sensitive to bias.
  • EE and TMLE estimators demonstrated improved efficiency and robustness.

Conclusions:

  • The CSDE is a valuable estimand for mediation analysis.
  • EE and TMLE are preferred estimators for CSDE due to robustness and efficiency.
  • These methods advance causal inference in observational and experimental studies.