Table 12. Average target domain Macro-F1 score of all algorithms on the ACS Mobility and US Accident datasets. In ACS Mobility, we test all algorithms on the 50 American states and report the mean and standard deviation across all 50 target states. And in US Accident, we choose 13 American states with relatively high sample sizes in testing. The standard deviation here reflects the stability of performances across different target states.
DatasetSource State	ACS Mobility	US Accident
NY	CA	MS	PA	CA	FL	TX	MN
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	0.451	0.427±0.015	0.462	0.446±0.026	0.531	0.488±0.020	0.527	0.488±0.020	0.800	0.678±0.147	0.898	0.703±0.133	0.936	0.736±0.092	0.855	0.703±0.110
SVM	0.604	0.572±0.015	0.592	0.550±0.014	0.635	0.532±0.018	0.623	0.581±0.018	0.793	0.674±0.114	0.891	0.698±0.093	0.928	0.698±0.093	0.880	0.669±0.078
MLP	0.600	0.583±0.020	0.622	0.607±0.018	0.607	0.583±0.012	0.608	0.578±0.020	0.827	0.755±0.103	0.923	0.769±0.090	0.934	0.720±0.094	0.886	0.713±0.096
Tree Ensemble Methods	Random Forest	0.570	0.530±0.019	0.587	0.563±0.023	0.641	0.534±0.022	0.635	0.561±0.025	0.846	0.752±0.077	0.932	0.832±0.096	0.938	0.834±0.095	0.911	0.800±0.058
LightGBM	0.637	0.589±0.014	0.625	0.601±0.019	0.671	0.590±0.021	0.660	0.590±0.015	0.851	0.768±0.075	0.929	0.831±0.094	0.940	0.833±0.096	0.918	0.786±0.054
XGBoost	0.637	0.591±0.018	0.625	0.605±0.018	0.678	0.586±0.023	0.664	0.596±0.017	0.852	0.786±0.072	0.942	0.812±0.088	0.939	0.833±0.095	0.920	0.771±0.052
DRO Methods	x²-DRO	0.613	0.613±0.019	0.599	0.579±0.015	0.540	0.514±0.031	0.606	0.583±0.017	0.824	0.754±0.099	0.922	0.721±0.121	0.933	0.747±0.091	0.883	0.704±0.095
CVaR-DRO	0.621	0.612±0.017	0.628	0.603±0.015	0.574	0.565±0.025	0.639	0.613±0.021	0.825	0.742±0.094	0.924	0.731±0.111	0.936	0.740±0.090	0.888	0.716±0.092
x²-DORO	0.509	0.501±0.011	0.619	0.612±0.018	0.530	0.505±0.013	0.540	0.508±0.014	0.815	0.707±0.104	0.920	0.760±0.089	0.927	0.734±0.092	0.874	0.727±0.080
CVaR-DORO	0.533	0.524±0.011	0.625	0.578±0.019	0.521	0.500±0.008	0.510	0.477±0.016	0.825	0.748±0.090	0.922	0.732±0.110	0.934	0.738±0.092	0.887	0.729±0.094
Group DRO	0.588	0.567±0.018	0.566	0.528±0.020	0.564	0.536±0.019	0.595	0.555±0.021	N/A	N/A	N/A	N/A	N/A	N/A	N/A
Imbalanced Learning Methods	SUBY	0.657	0.628±0.017	0.649	0.603±0.017	0.674	0.621±0.016	0.678	0.629±0.019	0.854	0.839±0.073	0.933	0.834±0.095	0.938	0.833±0.095	0.915	0.821±0.066
RWY	0.657	0.627±0.017	0.647	0.610±0.014	0.678	0.621±0.017	0.677	0.633±0.018	0.856	0.845±0.076	0.930	0.834±0.097	0.940	0.833±0.095	0.920	0.790±0.055
SUBG	0.635	0.589±0.016	0.624	0.601±0.020	0.672	0.595±0.020	0.658	0.592±0.018	0.847	0.790±0.073	0.925	0.826±0.094	0.937	0.833±0.095	0.906	0.784±0.057
RWG	0.637	0.587±0.018	0.621	0.584±0.016	0.676	0.582±0.018	0.663	0.596±0.018	0.855	0.775±0.074	0.929	0.825±0.089	0.938	0.833±0.095	0.919	0.768±0.051
JTJ	0.639	0.594±0.014	0.616	0.574±0.013	0.660	0.589±0.019	0.639	0.584±0.017	0.843	0.786±0.074	0.922	0.823±0.091	0.935	0.833±0.095	0.912	0.766±0.052
DWR	0.622	0.581±0.013	0.620	0.605±0.020	0.656	0.585±0.018	0.647	0.597±0.018	0.853	0.777±0.075	0.929	0.824±0.094	0.939	0.833±0.094	0.917	0.759±0.054
Fairness Methods	DP	0.630	0.588±0.014	0.621	0.590±0.014	0.663	0.590±0.020	0.668	0.593±0.018	0.847	0.776±0.073	0.922	0.814±0.085	0.933	0.832±0.095	0.915	0.799±0.059
EO	0.632	0.590±0.018	0.625	0.602±0.019	0.665	0.593±0.022	0.662	0.597±0.020	0.846	0.771±0.077	0.926	0.831±0.091	0.938	0.833±0.096	0.917	0.772±0.051
EP	0.633	0.596±0.017	0.620	0.598±0.017	0.671	0.581±0.015	0.660	0.593±0.017	0.852	0.787±0.071	0.928	0.830±0.094	0.937	0.832±0.097	0.914	0.773±0.049
Exp	0.633	0.589±0.015	0.622	0.594±0.020	0.669	0.595±0.020	0.663	0.595±0.016	0.837	0.769±0.079	0.893	0.808±0.085	0.909	0.803±0.091	0.854	0.784±0.060
Threshold	0.629	0.587±0.013	0.620	0.596±0.019	0.661	0.571±0.019	0.658	0.597±0.022	0.820	0.755±0.069	0.870	0.782±0.074	0.885	0.787±0.086	0.809	0.730±0.045