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arXiv · 2103.03857

Revisiting the g-null paradox

Sean McGrath·Jessica G. Young·Miguel A. Hernán
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Abstract

The parametric g-formula is an approach to estimating causal effects of sustained treatment strategies from observational data. An often cited limitation of the parametric g-formula is the g-null paradox: a phenomenon in which model misspecification in the parametric g-formula is guaranteed under the conditions that motivate its use (i.e., when identifiability conditions hold and measured time-varying confounders are affected by past treatment). Many users of the parametric g-formula know they must acknowledge the g-null paradox as a limitation when reporting results but still require clarity on its meaning and implications. Here we revisit the g-null paradox to clarify its role in causal inference studies. In doing so, we present analytic examples and a simulation-based illustration of the bias of parametric g-formula estimates under the conditions associated with this paradox. Our results highlight the importance of avoiding overly parsimonious models for the components of the g-formula when using this method.

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Sean McGrath, Jessica G. Young, Miguel A. Hernán. 2021-03-05. Revisiting the g-null paradox. https://doi.org/10.1097/ede.0000000000001431

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