  While standard meta-analysis pools the results from randomized trials that
compare two treatments, network meta-analysis aggregates the results of
randomized trials comparing a wider variety of treatment options. However, it
is unclear whether the aggregation of effect estimates across heterogeneous
populations will be consistent for a meaningful parameter when not all
treatments are evaluated on each population. Drawing from counterfactual theory
and the causal inference framework, we define the population of interest in a
network meta-analysis and define the target parameter under a series of
nonparametric structural assumptions. This allows us to determine the
requirements for identifiability of this parameter, enabling a description of
the conditions under which network meta-analysis is appropriate and when it
might mislead decision making. We then adapt several modeling strategies from
the causal inference literature to obtain consistent estimation of the
intervention-specific mean outcome and model-independent contrasts between
treatments. Finally, we perform a reanalysis of a systematic review to compare
the efficacy of antibiotics on suspected or confirmed methicillin-resistant
\emph{Staphylococcus aureus} in hospitalized patients.
