Beeswarm SHAP plot produces a different output at each execution
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Description
I train a CausalForestDML as follows;
est_forest = CausalForestDML(random_state = 123)
est_forest.fit(Y, T, X = W, W = W, inference = 'auto'),
and then compute the Shapley values:
shap_values = est_forest.shap_values(W).
This works fine, except that the beeswarm plot is different each time I execute:
shap.plots.beeswarm(shap_values['log_price_m2']['log_listings_iris'], max_display = 20).
I understand that this is a problem with the SHAP package, but still I think you would like to know about this unintended behavior, and may be you may have an explanation for it.
Thank you.
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Research direction
Start by reproducing the issue with CausalForestDML(random_state=123), est_forest.shap_values(W), and shap.plots.beeswarm(...). Inspect the EconML SHAP-value path and the SHAP beeswarm behavior to determine where the output changes between executions. Done means identifying whether EconML or SHAP causes the variation and documenting or correcting the behavior accordingly.
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Assessment
- Tech stack
- python
- Domain
- data-visualization, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100