CausalForestDML with binary outcome and treatment

Hi, I'm building a causal forest with binary outcome and binary treatment.
I have sufficient observations (over 100K) for two groups, but the model doesn't seem to work well because const_marginal_ate is -0.00152163 and feature_importances_ returns an array of zeros looking like array([0., 0., 0.,...

Could you let me know a) CausalForestDML is the right method to use for binary outcome and binary treatment and b) the model setting below is correct?

# set variables for causal forest 
Y = train[one variable]
T = train[one variable]
X = train[20 variables]
W = None
X_test = test[20 variables]

# set parameters for causal forest 
est = CausalForestDML(criterion='het',
                                min_impurity_decrease=0.001,
                                n_estimators=1000,       
                                min_samples_leaf=10, 
                                max_depth=None, 
                                max_samples=0.5,
                                discrete_treatment=True,
                                honest=True,
                                inference=True,
                                cv=5,
                                model_t=RandomForestClassifier(random_state=0), 
                                model_y=RandomForestClassifier(random_state=0),
                                )

# Fit the model
est.fit(Y, T, X=X, W=W)

Start at the CausalForestDML entry point and review the supplied fit configuration, especially binary outcome and treatment handling. Reproduce the reported const_marginal_ate and feature_importances_ results with the shown model settings, then determine whether the behavior is expected or indicates a defect. Done requires a documented maintainer conclusion or a narrowly defined change.