Comments (3)
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thanks for the clarification.
from dowhy.
I have a similar question:
estimates = model.estimate_effect(estimands, method_name = "backdoor.linear_regression")
print(estimate)
Output:
linear_regression
{'control_value': 0, 'treatment_value': 1, 'test_significance': None, 'evaluate_effect_strength': False, 'confidence_intervals': False, 'target_units': 'ate', 'effect_modifiers': []}
*** Causal Estimate ***
Identified estimand
Estimand type: nonparametric-ate
Estimand : 1
Estimand name: backdoor
Estimand expression:
d
─────────────────(E[Target])
d[X₁ X₂ X₃ X₄]
Estimand assumption 1, Unconfoundedness: If U→{X1,X2,X3,X4} and U→Target then P(Target|X1,X2,X3,X4,,U) = P(Target|X1,X2,X3,X4,)
Realized estimand
b: Target~X1+X2+X3+X4
Target units: ate
Estimate
Mean value: 7.982444233679164
What is Mean value: 7.982444233679164?
from dowhy.
Related Issues (20)
- Default Significance Level in Refutation Tests HOT 2
- test_stat_significance()['p_value'] HOT 3
- Refutation Test with Treatment Classes HOT 3
- Possible bug in the falsify dag notebook. HOT 5
- Expose interventional outcomes from do operator for further analysis HOT 2
- Update/refine causal model HOT 4
- interpretation of refutation test results and decision making HOT 4
- returning array([0.]) for p value of test_significance HOT 4
- attribute anomalies fails check on pandas 2.0.2 HOT 4
- graph learning and R depedency on CDT HOT 9
- Get error in refutation process ValueError: No group keys passed! HOT 5
- Move case studies to a location where those are freely accessible
- Treat each covariate as treatment one at a time in double ML HOT 2
- [MAINT] Refactor any dependencies on independence tests to use optional dependency `pywhy-stats`
- backdoor.propensity_score_weighting - pass options to LogisticRegression HOT 1
- estimate_effect() got an unexpected keyword argument 'num_quantiles_to_discretize_cont_cols'
- ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). HOT 1
- Support newer versions of Cython HOT 1
- Unexpected results for CATE methods when predicting on new data HOT 1
- Remove graph-learner API from DoWhy and point to causal-learn / dodiscover instead
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