  Researchers have proposed many methods for fair and robust machine learning,
but comprehensive empirical evaluation of their subgroup robustness is lacking.
In this work, we address this gap in the context of tabular data, where
sensitive subgroups are clearly-defined, real-world fairness problems abound,
and prior works often do not compare to state-of-the-art tree-based models as
baselines. We conduct an empirical comparison of several previously-proposed
methods for fair and robust learning alongside state-of-the-art tree-based
methods and other baselines. Via experiments with more than $340{,}000$ model
configurations on eight datasets, we show that tree-based methods have strong
subgroup robustness, even when compared to robustness- and fairness-enhancing
methods. Moreover, the best tree-based models tend to show good performance
over a range of metrics, while robust or group-fair models can show
brittleness, with significant performance differences across different metrics
for a fixed model. We also demonstrate that tree-based models show less
sensitivity to hyperparameter configurations, and are less costly to train. Our
work suggests that tree-based ensemble models make an effective baseline for
tabular data, and are a sensible default when subgroup robustness is desired.
For associated code and detailed results, see
https://github.com/jpgard/subgroup-robustness-grows-on-trees .
