RATE @u(↑)	Recall @u(↑)	Precision @u(↑)
0.999	.998	0.995	0.99	0.999	0.998	0.995	0.99	0.999	0.998	0.995	0.99
Random	0.00±<0.001	0.00±<0.001	0.00±<0.001	0.00±<0.001	0.0±<0.001	0.0±<0.001	0.01±<0.001	0.01±<0.001	0.01±<0.001	0.01±<0.001	0.01±<0.001	0.00±<0.001
T-learner	0.10±<0.001	0.07±<0.001	0.05±<0.001	0.04±<0.001	0.05±<0.001	0.07±<0.001	0.11±<0.001	0.13±<0.001	0.10±<0.001	0.08±<0.001	0.06±<0.001	0.04±<0.001
X-learner	0.00±<0.001	-0.01±<0.001	0.00±<0.001	0.00±<0.001	0.00±<0.001	0.00±<0.001	0.01±<0.001	0.02±<0.001	0.00±<0.001	0.00±<0.001	0.00±<0.001	0.01±<0.001
R-learner	-0.01±<0.001	-0.01±<0.001	-0.01±<0.001	0.00±<0.001	0.00±<0.001	0.00±<0.001	0.00±<0.001	0.04±<0.001	0.00±<0.001	0.00±<0.001	0.00±<0.001	0.01±<0.001
RA-learner	0.28±<0.001	0.26±<0.001	0.17±<0.001	0.10±<0.001	0.10±<0.001	0.19±<0.001	0.30±<0.001	0.37±<0.001	0.30±<0.001	0.28±<0.001	0.18±<0.001	0.11±<0.001
DragonNet	-0.01±0.002	0.00±0.009	0.00±0.004	0.00±0.003	0.00±<0.001	0.00±0.003	0.00±0.005	0.01±0.009	0.00±<0.001	0.00±0.010	0.00±0.004	0.00±0.003
TARNet	0.04±0.046	0.03±0.030	0.02±0.013	0.02±0.012	0.01±0.011	0.02±0.015	0.04±0.013	0.06±0.029	0.05±0.046	0.04±0.032	0.03±0.013	0.02±0.012
FlexTENet	0.02±0.024	0.02±0.019	0.04±0.012	0.03±0.013	0.01±0.009	0.03±0.018	0.08±0.012	0.12±0.037	0.02±0.027	0.03±0.020	0.04±0.012	0.04±0.014
S-learner w/ meta-learning	0.27±0.173	0.16±0.118	0.08±0.052	0.04±0.030	0.09±0.055	0.10±0.070	0.13±0.084	0.15±0.090	0.29±0.180	0.18±0.123	0.09±0.055	0.05±0.032
T-learner w/ meta-learning	0.27±0.173	0.16±0.118	0.08±0.052	0.04±0.030	0.09±0.055	0.10±0.070	0.13±0.084	0.15±0.090	0.29 ± 0.180	0.18±0.123	0.09±0.055	0.05±0.032
GraphITE	0.25±0.088	0.15±0.054	0.06±0.025	0.03±0.011	0.08±0.024	0.10±0.034	0.11±0.045	0.13±0.049	0.27±0.091	0.16±0.057	0.07±0.027	0.04±0.013
SIN	0.00±0.008	0.00±0.014	0.00±0.008	0.00±0.005	0.00±0.005	0.00±0.008	0.02±0.015	0.03±0.009	0.00±0.007	0.01±0.014	0.01±0.009	0.01±0.005
CaML - w/o meta-learning	0.45±0.070	0.38±0.057	0.21±0.017	0.13±0.008	0.19±0.019	0.28±0.026	0.38±0.025	0.45±0.019	0.49±0.070	0.41±0.057	0.23±0.017	0.15±0.008
CaML - w/o RA-learner	0.40±0.101	0.33±0.034	0.24±0.014	0.15±0.010	0.18±0.025	0.28±0.010	0.42±0.024	0.50±0.028	0.44±0.099	0.36±0.033	0.26±0.014	0.17±0.010
CaML (ours)	0.47±0.084	0.37±0.044	0.23±0.022	0.15±0.013	0.20±0.015	0.30±0.016	0.43±0.024	0.51±0.027	0.51±0.079	0.40±0.044	0.25±0.023	0.17±0.013
Table 5: Performance results for the medical claims dataset, in which the task is to predict the effect of a pair of drugs the drug on pancytopenia occurrence. Mean and standard deviation between runs is reported. Single-task methods were trained on the meta-testing tasks (best model underlined). Methods that were capable of training across multiple tasks were trained on meta-training tasks and applied to previously unseen meta-testing tasks (best model in bold). CaML outperforms the strongest baseline that had access to testing tasks on 12 out of 12 metrics, and outperforms all zero-shot baselines. Notably, due to the small sample size for natural experiments with combinations of drugs, the RATE estimation process is very noisy which is reflected in high variability of the measured RATE. Here, the secondary metrics (Recall and Precision) that are not affected, additionally assert the dominance of CaML over all baselines.