Comments (3)
You can access the statsmodel estimator object from a CausalEstimate object.
sm_est = estimate.estimator.model
sm_est.summary()
and then use summary() method to get the stats.
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Related Issues (20)
- Does this package support non-English languages? HOT 3
- Question about Dummy Outcome Refuter HOT 2
- Inconsistency in the placebo_treatment_refuter when using estimate_effect of IV HOT 1
- numpy.dual is dropped but it still occurs in dowhy HOT 2
- NetworkXError: graph should be directed acyclic HOT 4
- Refutation & Overlap Error ("data_subset_refuter", "add_unobserved_common_cause", assess_support_and_overlap_overrule) HOT 2
- No Backdoor Path Available
- Clarification on how to use gcm properly for confounders adjustment HOT 5
- Can you provide code demo for each function? HOT 2
- How is propensity score matching implemented? HOT 2
- Interpreting mean while using logistic regression to estimate causal effect. HOT 1
- model.estimate_effect and model.refute_astimate throws 'A column-vector y was passed ...' error
- RuntimeWarning: divide by zero encountered in divide when using evaluate_causal_model HOT 6
- Auto assign_causal_mechanisms is taking so much time in gcm HOT 12
- falsify_graph HOT 8
- Remove use of CausalModel from test files and notebooks
- Is there a way to work with a structural causal model where nodes have multiple values? For use in root cause analysis. HOT 8
- graph should be directed acyclic HOT 5
- dowhy replication issue and output discrepancy with statsmodels HOT 2
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