NonParamDML always returns constant effect for continuous treatment

I'm been seeing NonParamDML from econml 0.16.0 always returns constant effect for my data (continuous treatment, binary output). A toy example:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor

# Generate STRONGLY non-linear data
np.random.seed(123)
n = 2000
X_test = np.random.normal(size=(n, 4))
T_test = np.random.uniform(0, 6, size=(n, 1))

# Very strong non-linear effect - cubic with clear differences
true_effect = 0.5 * (T_test - 3)**3 + 2 * (T_test - 3)  # Cubic centered at 3
Y_logit = X_test.sum(axis=1, keepdims=True) + true_effect + np.random.normal(0, 0.3, (n, 1))
Y_prob = 1 / (1 + np.exp(-Y_logit))
Y_test = np.random.binomial(1, Y_prob.flatten()).reshape(-1, 1)

# Use your exact model specification
est_test = NonParamDML(
    model_y=RandomForestClassifier(n_estimators=200, max_depth=10),
    model_t=RandomForestRegressor(n_estimators=200, max_depth=10),
    model_final=RandomForestRegressor(n_estimators=200, max_depth=10),
    random_state=42,
    discrete_treatment=False,
    discrete_outcome=True,
    cv=3
)

est_test.fit(Y_test.ravel(), T_test.ravel(), X=X_test)

# Test on the same ranges
Xd_test = X_test[:100]
print("=== TESTING WITH SYNTHETIC NON-LINEAR DATA ===")
print(f"ATE (1→2): {est_test.ate(Xd_test, T0=1, T1=2)}")
print(f"ATE (2→3): {est_test.ate(Xd_test, T0=2, T1=3)}")
print(f"ATE (3→4): {est_test.ate(Xd_test, T0=3, T1=4)}")
print(f"ATE (4→5): {est_test.ate(Xd_test, T0=4, T1=5)}")

# These SHOULD be different if your setup is working

And output:

A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
=== TESTING WITH SYNTHETIC NON-LINEAR DATA ===
ATE (1→2): [0.23992668]
ATE (2→3): [0.23992668]
ATE (3→4): [0.23992668]
ATE (4→5): [0.23992668]

Is this behavior expected? I'm only seeing from EconML document of an example with binary treatment but can't find any example of continuous treatment.

Start at the NonParamDML entry point and rerun the provided synthetic example with continuous treatment and binary outcome. Trace how ate(T0, T1) handles the treatment range, then compare it with the documented binary-treatment example. Done means establishing whether equal effects are expected or reproducing a verified fix.