Multiple outcomes for DR

We're attempting to use DR methods on data with a single binary treatment effect (0 and 1) and multiple outcome variables. However, we've encountered an issue when trying to use the DRLearner with multiple outcomes. For example,

from econml.dr import DRLearner

T = [0, 0, 1]
X = [[-0.98,  0.19],[ 0.22,  0.82],[-0.77, -0.75]]
y = [[-0.47,  2.87],[ 2.11,  1.48], [-1.46,  2.19]]

obj = DRLearner(model_propensity=LogisticRegression(), model_regression=Ridge(), model_final=Ridge(), min_propensity=0.01, cv=2)
obj.fit(y, T, X=X)

returned the following error

ValueError: The outcome matrix must be of shape (3, ) or (3, 1), instead got (3, 2).

Is there a way to enable DR learners to support multiple outcomes? If not currently possible, are there any plans to add this functionality in future updates?

Thank you in advance.

Start by reproducing the example with econml.dr.DRLearner and trace the validation that rejects the (3, 2) outcome matrix. Determine the intended multi-outcome behavior and verify that fitting DRLearner with multiple outcomes completes successfully and returns the expected treatment-effect shape.