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This study presents a new statistical strategy for estimating causal effects with ordinal outcomes and binary mediators. The model provides exact parametric formulations for direct and indirect effects, improving upon previous approximations.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Causal Inference

Background:

  • Estimating causal effects is crucial in observational studies.
  • Existing methods for ordinal outcomes and binary mediators often rely on approximations.
  • There is a need for exact parametric formulations in causal mediation analysis.

Purpose of the Study:

  • To develop a model-based strategy for estimating counterfactual direct and indirect effects.
  • To address situations with an ordinal response variable and a binary mediator.
  • To provide exact parametric formulations for causal effects, extending prior research.

Main Methods:

  • Postulation of a logistic regression model for the binary mediator.
  • Utilization of a cumulative logit model for the ordinal outcome variable.
  • Derivation of exact parametric formulations for causal effects and natural effect models.

Main Results:

  • The proposed method yields exact parametric formulations for direct and indirect effects.
  • Identification conditions are consistent with established literature.
  • Causal effects can be estimated using standard statistical software with bootstrap standard errors.

Conclusions:

  • The developed methodology offers an exact approach to causal mediation analysis for ordinal outcomes.
  • The strategy is accessible and implementable using standard statistical software, including R routines.
  • This work advances the field by providing precise causal effect estimations in complex scenarios.