More transparent error messages when calling effect functions on un-fitted estimators
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Description
Currently we throw somewhat obscure error messages when users try to call something like est.effect() on an estimator that hasn't been fit yet.
May be better to have a clearer error message.
Repro:
from econml.dml import LinearDML
LinearDML().effect()
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First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
Start by running the reported LinearDML().effect() reproduction and trace the effect entry point for an unfitted estimator. Add a regression test covering this call and make the resulting error message clearly explain that fitting is required; done means the test passes with the clearer message.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 58/100