Exact parametric causal mediation analysis for a binary outcome with a binary mediator

Marco Doretti (), Martina Raggi and Elena Stanghellini
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Marco Doretti: University of Perugia
Martina Raggi: University of Neuchâtel

Statistical Methods & Applications, 2022, vol. 31, issue 1, No 5, 87-108

Abstract: Abstract With reference to causal mediation analysis, a parametric expression for natural direct and indirect effects is derived for the setting of a binary outcome with a binary mediator, both modelled via a logistic regression. The proposed effect decomposition operates on the odds ratio scale and does not require the outcome to be rare. It generalizes the existing ones, allowing for interactions between both the exposure and the mediator and the confounding covariates. The derived parametric formulae are flexible, in that they readily adapt to the two different natural effect decompositions defined in the mediation literature. In parallel with results derived under the rare outcome assumption, they also outline the relationship between the causal effects and the correspondent pathway-specific logistic regression parameters, isolating the controlled direct effect in the natural direct effect expressions. Formulae for standard errors, obtained via the delta method, are also given. An empirical application to data coming from a microfinance experiment performed in Bosnia and Herzegovina is illustrated.

Keywords: Causal inference; Direct and indirect effects; Effect decomposition; Logistic regression; Mediation analysis; Odds ratio (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10260-021-00562-w

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