Inverse probability weights are commonly used in research to estimate treatment effects, but poor overlap between treated and control groups can lead to biased results. Balancing weights, which target imbalances directly, can help target the treatment effect on the treated even with poor overlap. This approach was tested in three simulation studies and an empirical application, showing that balancing weights can be effective in these situations.