DML: Should the treatment effect be constant at a given X, over various values of the treatment?

Hello.

I am running the example here: https://github.com/microsoft/EconML/blob/master/notebooks/CustomerScenarios/Case%20Study%20-%20Customer%20Segmentation%20at%20An%20Online%20Media%20Company.ipynb

I am confused by what is being estimated I think. After this

est = CausalForestDML(
    model_y=GradientBoostingRegressor(), model_t=GradientBoostingRegressor()
)
est.fit(log_Y, log_T, X=X, W=W, inference="blb")

I expected that since this was a non-parametric estimator, compared to linear DML that the treatment effect should differ at various values for T given a constant X.

When I run these, which I think are producing the CATE as the treatment goes from T0 to T1 at the given X values for each observation, they are all the same. These are all 1 unit apart for T0 and T1.

est.effect(X_test, T0=-0.22, T1=.78)
est.effect(X_test, T0=1.22, T1=2.22)

Further, if I produce what I think is the slope of the CATE at a given value of a treatment, for a fixed X, they are all the same:

est.marginal_effect(T= -0.22, X = X_test)
est.marginal_effect(T= 1, X = X_test)

Likewise if I produce the ATE for a given X, which I think are simply the mean of the above (est.effect(X_test, T0=0, T1=1)) for different values of T0 and T1, the results are all the same, The average marginal is the same too regardless of the value of T it is evaluated at....

est.ate(T0= 0 , T1 =1 ,X = X_test)
est.ate(T0= -1 , T1 =0 ,X = X_test)

est.marginal_ate(T= -0.22, X = X_test)
est.marginal_ate(T= 0, X = X_test)

Are my understandings incorrect that these should differ?

Start with the Customer Segmentation notebook and the CausalForestDML calls shown in the issue; compare the semantics of effect, marginal_effect, ate, and marginal_ate at different treatment values. Done means documenting whether the observed constant results are expected and clarifying the example or API guidance accordingly.