What is the proper way to calculate ATT and ATC using EconML estimators?

Is there a way to calculate ATT and ATC using EconML estimators directly without using the DoWhy wrapper?
In the below codes, I tried DoWhy wrapper but the ATT and ATC estimates do not look intuitive. The truthful values in my synthetic dataset are:

ATE=2.150835273467599
ATT=2.7547937877727984
ATC=2.0194467325766614

Ignoring the estimation error, I was expecting ATT to be greater than ATC, and ATE is in between. The DoWhy wrapper results did not show that.

estimator = LinearDML(model_y=RandomForestRegressor(), model_t=RandomForestRegressor(), random_state=123)
estimator.fit(Y=pd_table[outcome], T=pd_table[treatment], W=pd_table[common_causes])
print(f"EconML ATE = {estimator.effect()[0]}")
est1 = estimator.dowhy
est1.fit(Y=pd_table[outcome], T=pd_table[treatment], W=pd_table[common_causes], target_units="ate")
print(f"DoWhyWrapper ATE = {est1.estimate_.value}")
est2 = estimator.dowhy
est2.fit(Y=pd_table[outcome], T=pd_table[treatment], W=pd_table[common_causes], target_units="att")
print(f"DoWhyWrapper ATT = {est2.estimate_.value}")
est3 = estimator.dowhy
est3.fit(Y=pd_table[outcome], T=pd_table[treatment], W=pd_table[common_causes], target_units="atc")
print(f"DoWhyWrapper ATC = {est3.estimate_.value}")

--outputs--
EconML ATE = 2.6725369948454785
DoWhyWrapper ATE = 2.671863536095423
DoWhyWrapper ATT = 2.669885836212373
DoWhyWrapper ATC = 2.6640237888067766

The entry points mentioned are LinearDML and its dowhy wrapper, with target_units set to "ate", "att", and "atc"; start by checking their API documentation and target-unit semantics. Done means establishing whether the reported values are expected for the synthetic data and documenting or correcting the discrepancy.