# Dowhy causal inference estimates equal from two different models - why?

I am using a python package based on judea pearls book of why. I Tried two different models which i have depicted below, these structures would go in the digraph section of the code below. After running the code below for these two different graphical models i get exactly the same estimate. My question is how is this happening?

causal_graph = """digraph {

}"""

#print(df_dowhy)
model = dowhy.CausalModel(data=df_dowhy,
graph=causal_graph.replace("\n", " "),
treatment="drug",
outcome="outcome")
model.view_model()
from IPython.display import Image, display
display(Image(filename="causal_model.png"))

identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)

estimate = model.estimate_effect(identified_estimand,
method_name='backdoor.propensity_score_stratification',
target_units="att")
print(estimate)


I don't understand how these backdoor variables (in grey) are being taken into account if remvoing them gives me same estimate (model i tested on right). Since i am using propensity score stratification could it be that the same control and treated groups are being selected?

I am using backdoor criterion and propensity score stratification for this- I thought in backdoor criterion all the variables in grey are conditioned on, but since i get exactly the same estimate for model on right (omitting grey variables) doesn't this imply the estimand is the exact same for both and it's disregarding the backdoor variables?