Using Balancing Weights to Target the Treatment Effect on the Treated when Overlap is Poor

TL;DR

This paper investigates the use of balancing weights to accurately estimate the treatment effect on the treated in observational studies, especially when poor overlap causes issues with traditional inverse probability weighting.

Contribution

It demonstrates that balancing weights can effectively target the average treatment effect on the treated even under poor overlap conditions, offering an alternative to inverse probability weights.

Findings

Balancing weights enable targeting the average treatment effect on the treated despite poor overlap.

They produce less biased estimates compared to inverse probability weights in such scenarios.

Balancing weights can be used to target more familiar estimands than those targeted by overlap weights.

Abstract

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.