Comments (4)
Hi, I'm wondering does an estimate has to pass all the possible refutation tests to give us confidence say "this estimation is not problematic?" my situation is: the estimation passed random cause, subset and bootstrap, but only failed placable. Should I trust this estimate?
From another threads, I saw the author suggested placebo test is more about the estimator. Should I change the estimator to see if it passes?
Thanks!
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These refutations are necessary tests. So, a good analysis should pass all the tests. So if your estimator fails the placebo test, there is something wrong. Placebo test is not just about the estimator, it points to an error anywhere in your analysis: either in the modeling stage (Graph) or the estimation stage.
In practice, changing the estimator is a good first step to debug. If multiple estimators fail the test, you may need to look at your graph too and make sure that it is correct.
For more information, you can refer to the user guide: https://www.pywhy.org/dowhy/v0.10/user_guide/refuting_causal_estimates/index.html
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This issue is stale because it has been open for 14 days with no activity.
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This issue was closed because it has been inactive for 7 days since being marked as stale.
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Related Issues (20)
- 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
- how to show summary stats (R squared, p value, F stat) from causal estimate HOT 3
- dowhy 0.11.1 dependences looks not being loaded HOT 2
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