CausalForest DML Randomness
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Description
Hi, we recently found that we get different prediction results every time we run the causal forest model with ite_estimates = model.effect(df[features])
ite_ci = model.effect_interval(df[features])
The model was pre-trained, saved and then loaded for predictions.
Is it because the effect() and effect_interval() methods often use bootstrap sampling to generate predictions, which introduces randomness?
How could I reproduce predictions with a CausalForest DML model?
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Research direction
Start with the CausalForest DML effect() and effect_interval() calls described in the issue, then reproduce them after saving and loading the pre-trained model. Compare repeated predictions and determine whether the behavior is expected; done means documenting or correcting how reproducible predictions should be obtained.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100