Effect Interval for DR Methods Not Possible When T0 is non-zero
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Description
I have 10 treatment levels 0,1,2,...,9. I wanted the confidence interval for treatment effects given a X value using the effect_interval function. I ran the code effect_interval(X=X[:1],T0=1,T1=2).
The above function is working fine for DML algorithms but while using DR algorithms it is throwing the following error:
AttributeError: Can only calculate inference of effects between a non-baseline treatment and the baseline treatment!
In the documentation effect_interval is meant to work for different base treatment levels as the user wants.
Another point is that, the above issue is not occurring while using the effect function.
So, how can I get this issue solved?
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- Read the whole issue, then the project's contributing guide.
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Research direction
Start by reproducing the reported effect_interval(X=X[:1], T0=1, T1=2) call with a DR algorithm, then compare its behavior with the effect function and the documented effect_interval semantics. Done means effect_interval can return an interval for non-zero treatment levels without raising the baseline-treatment error.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100