  There is growing interest in exploring causal effects in target populations
by combining multiple datasets. Nevertheless, most approaches are tailored to
specific settings and lack comprehensive comparative analyses across different
settings. In this article, within the typical scenario of a source dataset and
a target dataset, we establish a unified framework for comparing various
settings in causal inference via data combination. We first design six distinct
settings, each with different available datasets and identifiability
assumptions. The six settings cover a wide range of scenarios in the existing
literature. We then conduct a comprehensive efficiency comparative analysis
across these settings by calculating and comparing the semiparametric
efficiency bounds for the average treatment effect (ATE) in the target
population. Our findings reveal the key factors contributing to efficiency
gains or losses across these settings. In addition, we extend our analysis to
other estimands, including ATE in the source population and the average
treatment effect on treated (ATT) in both the source and target populations.
Furthermore, we empirically validate our findings by constructing locally
efficient estimators and conducting extensive simulation studies. We
demonstrate the proposed approaches using a real application to a MIMIC-III
dataset as the target population and an eICU dataset as the source population.
