y should be a 1d array ERROR

Hi,

i am trying to run econml methods on 'multiple methods';

with continuous variables

there seems to be no problem when running the 'linear estimate'; however, i get the 'y should be a 1d array, got an array of shape (66516, 4) instead.' when running dml.dml or other methods.

i am trying to use the following code which works for backdoor.linearregression;

dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML",
control_value = (0,0,0,0),
treatment_value = (1,1,1,1),
target_units = 1, # condition used for CATE
confidence_intervals=False,
method_params={"init_params":{'model_y':GradientBoostingRegressor(),
'model_t': GradientBoostingRegressor(),
"model_final":LassoCV(fit_intercept=False),
'featurizer':PolynomialFeatures(degree=1, include_bias=True)},
"fit_params":{}})

Here, the model is :
model=CausalModel(
data = pmid_regress2,
treatment= ['v1', 'v2', 'v3', 'v4'],
outcome= ['relative_citation_ratio'],
graph=graph
)

with pmid_regress2 being a dataframe; and outcome a specific column

Regards,

Start by reproducing the reported shape error through model.estimate_effect with backdoor.econml.dml.DML, then compare it with the working backdoor.linearregression path. Inspect how CausalModel receives the multi-column treatment and single-column outcome, and trace the DML entry point; done means the cause and supported input shape are confirmed and reflected in the issue or documentation.