Comments (7)
from dowhy.
Thank you. Any chance somebody also implemented the Adjustment Criterion by Shpitser (here, Definition 5)? This is a complete criterion for identification via covariate adjustment.
from dowhy.
Thanks @djinnome ! Definitely, implementing the ID algorithm is on our priority list, but being delayed with work on refutations. Would you be willing to contribute to the development?
Yes, I really the idea behind Whittemore--had a chance to talk to Joshua (who built Whittemore) a few weeks back.
from dowhy.
Sounds great. Let me know once you are ready. Happy to chat in case you need you need more details on the library's code structure.
from dowhy.
Hi all, was there meanwhile any progress on this front?
from dowhy.
yes, the ID algorithm is now implemented for identification.
https://microsoft.github.io/dowhy/example_notebooks/identifying_effects_using_id_algorithm.html
However, estimation based on the probability expressions returned by ID algorithm is not yet supported.
from dowhy.
That's not implemented, but it will a simple exercise to implement it by extending the backdoor criterion. Would you like to implement it @gianlucadetommaso ? I've added an issue #402
from dowhy.
Related Issues (20)
- Python 3.12 support HOT 9
- Clarify the differences among refute methods HOT 11
- Feature relevance/Influence HOT 26
- Graphviz installation : --include-path not recognized anymore HOT 4
- 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 3
- Auto assign_causal_mechanisms is taking so much time in gcm HOT 11
- falsify_graph HOT 7
- Remove use of CausalModel from test files and notebooks
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from dowhy.