Questions on implementation of econML DML

[abhilasha-workday]

Hello,

I am currently using DoWhy library for some causal analysis where my treatment is continuous and my outcome is binary. For estimating effect I used tow methods: Logistic regression through GLM and DML. Please find code snippets for their implementation:

Logistic Regression
import statsmodels.api as sm
estimate = model.estimate_effect(est_ident,
method_name="backdoor.generalized_linear_model",
test_significance=True,
method_params = {
'num_null_simulations':20,
'num_simulations':20,
'num_quantiles_to_discretize_cont_cols':10,
'fit_method': "statsmodels",
'glm_family': sm.families.Binomial(), # logistic regression
'need_conditional_estimates':False
},
control_value= 0.2,
treatment_value= 0.3
)
print(estimate)

DML
from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor
from sklearn.linear_model import LassoCV
from sklearn.preprocessing import PolynomialFeatures

dml_estimate = model.estimate_effect(est_ident, method_name="backdoor.econml.dml.DML",
control_value = 0.1,
treatment_value = 0.2,
confidence_intervals=False,
method_params={"init_params":{'model_y':GradientBoostingClassifier(random_state = 101),
'model_t': GradientBoostingRegressor(random_state = 101),
"model_final":LassoCV(random_state = 101),
'featurizer':PolynomialFeatures(degree=1, include_bias=True),
'discrete_treatment': False,
'random_state': 101
},
"fit_params":{}})
print(dml_estimate)

1. Could you elaborate on how to interpret ATE for both of these methods? For ex: If my ATE is -0.02, is it okay to say that 'Increasing treatment from 0.1 to 0.2, leads to 3% decrease in the outcome'?
2. Is the ATE returned by Logistic regression model actually the coefficient of the treatment variable of the model? Also, when I compare the coefficient returned by estimate.estimator.model for GLM estimator with the mean estimate returned by estimate_effect(), they are drastically different. Is that expected behavior?
3. I wanted to create a sort of a dose-response curve to see the effect of change of treatment on my outcome. For ex- how my outcome changes when I increase by treatment from 0.1-0.2, 02-0.3, 0.3-0.4 etc. In order to accomplish this, I changed the values for my control_value and treatment_value parameters in estimate_effect(). When I do so, I get different ATE with logistic regression but same ATE with DML. Why is that?
4. How does DoWhy work in the back to calculate ATE when the control_value and the treatment_value is provided? Specially interested in the GLM and DML effect estimation methods.

Looking forward to hearing back on this. TIA!