Comparison of confidence intervals between CausalForestDML and the R grf package
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Description
I trained the same model, both with the econml CausalForestDML and the R grf causal_forest algorithms.
The average marginal effects obtained are comparable, around 0.12. The confidence intervals are however quite different: [0.064,0.166] with causal_forest, and [-0.360,0.621] with CausalForestDML.
Do you have an idea of why there is such a difference?
Thanks
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Research direction
Start by reproducing the reported confidence intervals with Python's CausalForestDML and the R grf causal_forest algorithm using the same model setup. Compare how each entry point computes confidence intervals while checking that the average marginal effects remain comparable. Done means explaining the discrepancy or identifying a reproducible implementation issue.
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Assessment
- Tech stack
- python, r
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100