  In the context of missing data, the identifiability or "recoverability" of
the average causal effect (ACE) depends on causal and missingness assumptions.
The latter can be depicted by adding variable-specific missingness indicators
to causal diagrams, creating "missingness-directed acyclic graphs" (m-DAGs).
Previous research described ten canonical m-DAGs, representing typical
multivariable missingness mechanisms in epidemiological studies, and determined
the recoverability of the ACE in the absence of effect modification. We extend
the research by determining the recoverability of the ACE in settings with
effect modification and conducting a simulation study evaluating the
performance of widely used missing data methods when estimating the ACE using
correctly specified g-computation, which has not been previously studied.
Methods assessed were complete case analysis (CCA) and various multiple
imputation (MI) implementations regarding the degree of compatibility with the
outcome model used in g-computation. Simulations were based on an example from
the Victorian Adolescent Health Cohort Study (VAHCS), where interest was in
estimating the ACE of adolescent cannabis use on mental health in young
adulthood. In the canonical m-DAGs that excluded unmeasured common causes of
missingness indicators, we derived the recoverable ACE if no incomplete
variable causes its missingness, and non-recoverable otherwise. Besides, the
simulation showed that compatible MI approaches may enable approximately
unbiased ACE estimation, unless the outcome causes its missingness or it causes
the missingness of a variable that causes its missingness. Researchers must
consider sensitivity analysis methods incorporating external information in the
latter setting. The VAHCS case study illustrates the practical implications of
these findings.
