  Inverse probability weights are commonly used in epidemiology to estimate
causal effects in observational studies. Researchers can typically focus on
either the average treatment effect or the average treatment effect on the
treated with inverse probability weighting estimators. However, when overlap
between the treated and control groups is poor, this can produce extreme
weights that can result in biased estimates and large variances. One
alternative to inverse probability weights are overlap weights, which target
the population with the most overlap on observed characteristics. While
estimates based on overlap weights produce less bias in such contexts, the
causal estimand can be difficult to interpret. One alternative to inverse
probability weights are balancing weights, which directly target imbalances
during the estimation process. Here, we explore whether balancing weights allow
analysts to target the average treatment effect on the treated in cases where
inverse probability weights are biased due to poor overlap. We conduct three
simulation studies and an empirical application. We find that in many cases,
balancing weights allow the analyst to still target the average treatment
effect on the treated even when overlap is poor. We show that while overlap
weights remain a key tool for estimating causal effects, more familiar
estimands can be targeted by using balancing weights instead of inverse
probability weights.
