Table 11. Average target domain Macro-F1 score of all algorithms on the ACS Income and ACS Pub. Cov datasets. In each setting, we test all algorithms on the 50 American states and report the mean and standard deviation across all 50 target states. The standard deviation here reflects the stability of performances across different target states.
DatasetSource State	ACS Income	ACS Pub. Cov
CA	CT	MA	IN	FL	TX	NE	IN
i.d.	o.o.d	i.d.	o.o.d	i.d.	o.o.d	i.d.	o.o.d	i.d.	o.o.d	i.d.	o.o.d	i.d.	o.o.d	i.d.
Base Methods	LR	0.804	0.757±0.032	0.807	0.747±0.035	0.809	0.752±0.034	0.714	0.692±0.019	0.652	0.620±0.050	0.644	0.580±0.052	0.653	0.612±0.046	0.698	0.659±0.044
SVM	0.795	0.726±0.037	0.790	0.709±0.039	0.793	0.718±0.041	0.698	0.664±0.023	0.663	0.623±0.044	0.665	0.614±0.044	0.698	0.611±0.043	0.696	0.650±0.032
MLP	0.814	0.751±0.038	0.814	0.753±0.036	0.816	0.755±0.037	0.734	0.729±0.018	0.709	0.664±0.049	0.713	0.664±0.049	0.698	0.644±0.046	0.716	0.680±0.038
Tree Ensemble Methods	Random Forest	0.809	0.759±0.032	0.812	0.755±0.034	0.814	0.758±0.034	0.687	0.673±0.024	0.715	0.659±0.055	0.727	0.651±0.058	0.729	0.648±0.046	0.746	0.691±0.039
LightGBM	0.811	0.757±0.035	0.821	0.753±0.037	0.823	0.759±0.037	0.732	0.718±0.021	0.730	0.670±0.053	0.739	0.665±0.057	0.745	0.663±0.048	0.750	0.704±0.034
XGBoost	0.814	0.651±0.049	0.819	0.751±0.036	0.822	0.757±0.036	0.723	0.708±0.021	0.723	0.673±0.051	0.730	0.659±0.058	0.755	0.667±0.042	0.747	0.701±0.038
DRO Methods	\( \chi^2-DRO \)	0.813	0.754±0.036	0.812	0.752±0.036	0.816	0.748±0.039	0.732	0.737±0.019	0.704	0.659±0.044	0.697	0.657±0.048	0.685	0.646±0.038	0.715	0.673±0.030
CVaR-DRO	0.808	0.750±0.038	0.812	0.749±0.036	0.820	0.752±0.037	0.730	0.726±0.021	0.693	0.649±0.046	0.706	0.655±0.049	0.668	0.658±0.021	0.700	0.675±0.036
\( \chi^2-DORO \)	0.801	0.756±0.032	0.804	0.738±0.041	0.812	0.744±0.040	0.717	0.711±0.018	0.526	0.511±0.013	0.518	0.481±0.021	0.526	0.491±0.016	0.516	0.487±0.021
CVaR-DORO	0.802	0.746±0.037	0.804	0.740±0.041	0.811	0.740±0.041	0.720	0.706±0.020	0.660	0.619±0.044	0.675	0.629±0.052	0.559	0.541±0.025	0.649	0.623±0.023
Group DRO	0.812	0.760±0.033	0.813	0.754±0.035	0.817	0.753±0.037	0.741	0.742±0.018	0.706	0.665±0.051	0.717	0.660±0.050	0.700	0.635±0.042	0.419	0.411±0.030
Imbalanced Learning Methods	SUBY	0.807	0.739±0.041	0.815	0.748±0.039	0.820	0.750±0.040	0.719	0.739±0.025	0.710	0.699±0.023	0.710	0.692±0.027	0.704	0.682±0.031	0.748	0.710±0.025
RWY	0.812	0.747±0.041	0.818	0.747±0.039	0.821	0.753±0.039	0.733	0.732±0.020	0.725	0.705±0.025	0.729	0.699±0.032	0.736	0.680±0.031	0.749	0.709±0.024
SUBG	0.813	0.759±0.034	0.816	0.753±0.036	0.820	0.758±0.037	0.726	0.715±0.021	0.719	0.671±0.048	0.731	0.662±0.053	0.739	0.666±0.041	0.742	0.686±0.034
RWG	0.814	0.760±0.035	0.819	0.754±0.036	0.822	0.757±0.036	0.726	0.723±0.019	0.723	0.667±0.051	0.733	0.661±0.057	0.748	0.672±0.045	0.746	0.700±0.035
JTT	0.777	0.728±0.036	0.781	0.733±0.030	0.79	0.739±0.031	0.710	0.702±0.017	0.711	0.690±0.027	0.726	0.691±0.038	0.727	0.669±0.036	0.718	0.669±0.031
DWR	0.812	0.759±0.035	0.816	0.755±0.034	0.821	0.756±0.037	0.733	0.719±0.019	0.721	0.668±0.047	0.731	0.661±0.057	0.732	0.676±0.041	0.741	0.697±0.033
Fairness Methods	DP	0.807	0.746±0.037	0.810	0.740±0.038	0.817	0.752±0.037	0.716	0.708±0.017	0.727	0.677±0.049	0.729	0.659±0.054	0.750	0.668±0.043	0.742	0.698±0.032
EO	0.813	0.757±0.035	0.818	0.752±0.036	0.820	0.757±0.037	0.721	0.709±0.020	0.724	0.668±0.054	0.734	0.661±0.052	0.737	0.671±0.044	0.748	0.704±0.031
EP	0.811	0.759±0.035	0.819	0.752±0.037	0.821	0.756±0.037	0.723	0.709±0.018	0.724	0.668±0.051	0.731	0.657±0.058	0.747	0.671±0.039	0.748	0.699±0.034
Exp	0.808	0.750±0.037	0.810	0.737±0.038	0.818	0.749±0.037	0.706	0.691±0.021	0.724	0.667±0.053	0.729	0.656±0.057	0.738	0.661±0.049	0.741	0.699±0.033
Threshold	0.766	0.709±0.037	0.768	0.718±0.032	0.771	0.716±0.028	0.665	0.669±0.019	0.722	0.669±0.050	0.717	0.652±0.056	0.714	0.644±0.040	0.728	0.681±0.031