Outcomes shape handling in predict_dte #147

from dte_adj import SimpleDistributionEstimator

X = np.zeros((4, 1))
D = np.array([0, 0, 1, 1])
Y = np.array([0., 1., 2., 3.])

for outcomes in (Y, Y[:, None]):
    est = SimpleDistributionEstimator().fit(X, D, outcomes)
    effect, _, _ = est.predict_dte(1, 0, np.array([1.]), display_progress=False)
    print(effect.shape, effect.ravel())
# (1,)   [-1.]
# (4, 1) [0. 0. 0. 0.]```