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Related Experiment Video

Updated: Jun 17, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

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Published on: September 27, 2019

Marginal structural models for sufficient cause interactions.

Tyler J Vanderweele1, Stijn Vansteelandt, James M Robins

  • 1Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA. tvanderw@hsph.harvard.edu

American Journal of Epidemiology
|January 14, 2010
PubMed
Summary
This summary is machine-generated.

Marginal structural models offer a more plausible approach to testing sufficient cause interactions by relaxing restrictive assumptions of outcome regression models. This method also addresses time-dependent confounding and can estimate the prevalence of these interactions.

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Last Updated: Jun 17, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Area of Science:

  • Causal inference
  • Epidemiology
  • Biostatistics

Background:

  • Sufficient cause interactions occur when a causal mechanism requires multiple specific causes.
  • Existing empirical methods for testing sufficient cause interactions often rely on outcome regression models.
  • Outcome regression models impose potentially implausible assumptions on the relationship between confounders and background causes.

Purpose of the Study:

  • To introduce and evaluate the use of marginal structural models (MSMs) for testing sufficient cause interactions.
  • To address limitations of outcome regression models in controlling for confounding in sufficient cause interaction analysis.
  • To explore the utility of MSMs in scenarios with time-dependent confounding.

Main Methods:

  • Utilized marginal structural models (MSMs) as an alternative to traditional outcome regression models.
  • Focused on assumptions concerning the relationship between causes of interest and confounding variables.
  • Investigated the capacity of MSMs to handle time-dependent confounding.

Main Results:

  • MSMs relax restrictive assumptions inherent in outcome regression models, leading to more plausible modeling.
  • MSMs facilitate the testing of sufficient cause interactions in the presence of time-dependent confounding.
  • MSMs can be employed to estimate lower bounds on the prevalence of sufficient cause interactions.

Conclusions:

  • Marginal structural models provide a more flexible and often more plausible framework for testing sufficient cause interactions.
  • The MSM approach effectively addresses challenges posed by time-dependent confounding in causal analyses.
  • MSMs offer a dual benefit of testing for interactions and estimating their prevalence.