Comments (13)
Yes, in fact I agree that it's not worth digging into the details of the packages. My hunch is that there is a problem with the downgrading of some packages forced by anaconda=5.3.1. In any case, for myself I won't use anaconda and spyder anymore since they seem to introduce unnecessary complications.
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Thanks for raising this @mgralle ! I am trying to understand the source of the error. When I try this script on my local machine, I obtain the same value each time (1671.13) for linear regression.
Can you share samples of the different estimate values that you got?
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Thanks for paying attention to this problem!
When running my script on python 3.7 in Spyder 3.3.1 in Anaconda, I get the following estimated values from linear regression:
(1) Causal estimate from linear regression is 1671.1308841235102
(2) Causal estimate from linear regression is 937.2998778128333
(3) Causal estimate from linear regression is -333.2514015372243
Re-running the entire script, I get:
(1) Causal estimate from linear regression is 1671.1308841235102
(2) Causal estimate from linear regression is -782.0656222357087
(3) Causal estimate from linear regression is -861.948525208911
After exiting Spyder and re-entering, I get:
(1) Causal estimate from linear regression is 1671.1308841235111
(2) Causal estimate from linear regression is 741.7360515239643
(3) Causal estimate from linear regression is -74.26662536330865
Hope that helps!
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unable to recreate on python3.6. Making a fresh install of python3.7 to test your script there
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on a fresh install of python3.7, i get
(1) Causal estimate from linear regression is 1671.130884123515
(2) Causal estimate from linear regression is 1671.130884123515
(3) Causal estimate from linear regression is 1671.130884123515
so can't reproduce. @mgralle can you provide minimal steps to reproduce in a fresh venv?
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perfect! Thanks so much.
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Thanks @mgralle for spending the time. Let us know when you have an update.
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great! It sounds like the solution might be to say dowhy requires anaconda>5.3.1
, and fixing package versions in requirements.txt
to ones that are tested to work. The alternative would be to dig deep into details of which packages are failing, and that's a lot of time that could be spent on higher priorities (other bug fixes; adding features; documentation; tech debt). Do you agree?
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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 5
- 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 4
- 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
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