returning array([0.]) for p value of test_significance

[ianisadreamer]

Hi,

I used estimate.test_stat_significance() to see if the effect is significantly not close to 0, and it returns non-meaningful result as belows:

{'p_value': array([0.])}

What does it mean? is there anything wrong?

[amit-sharma]

Can you post a minimum working example so we can reproduce the bug?

[ianisadreamer]

Thank you! Do you think this is enough?

`from sklearn import preprocessing
#create instance of label encoder
lab = preprocessing.LabelEncoder()

#perform label encoding
tmp_clean['is_matched'] = lab.fit_transform(tmp_clean['is_matched'])
tmp_clean['work_type'] = lab.fit_transform(tmp_clean['work_type'])
tmp_clean['report_violation_type']= lab.fit_transform(tmp_clean['report_violation_type'])
tmp_clean['market_reporting'] = lab.fit_transform(tmp_clean['market_reporting'])
tmp_clean['detection_type'] = lab.fit_transform(tmp_clean['detection_type'])
tmp_clean['big_mac_segments'] = lab.fit_transform(tmp_clean['big_mac_segments'])
tmp_clean['is_admitted'] = lab.fit_transform(tmp_clean['is_admitted'])

from dowhy import CausalModel

model=CausalModel(
data = tmp_clean,
treatment='is_matched',
outcome='decision_handle_time',
common_causes= ['work_type', 'market_reporting', 'report_violation_type', 'detection_type', 'big_mac_segments', 'is_admitted']
)
model.view_model()

estimands = model.identify_effect()
print(estimands)

estimate = model.estimate_effect(estimands,method_name="backdoor.linear_regression",
test_significance=True)
print(estimate)

estimate.test_stat_significance()`

[ianisadreamer]

@amit-sharma It constantly returns {'p_value': array([0.])} to me.
I actually found when I manually built another causal graph and send it to the CausalModel(), it will return the normal P_value. I'm wondering if it's related to the common_causes used in the CausalModel or the graph itself.

[amit-sharma]

I see, it may be due to the dataset too. To reproduce the error, I will need the data too. Can you share the tmp_clean dataframe? If that is not possible, please share a simulated dataset that reproduces the problem.