Results changes after each run
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Description
Thanks for the very nice work!
I am running a basic model as the codes given below. I am getting different results when running these codes each time even though with random_state is fixed to 504
est = LinearDMLCateEstimator(model_y=RandomForestRegressor(),model_t=RandomForestRegressor(),n_splits= 2, random_state= 504)
est.fit(Y, T, X, W,inference='statsmodels')
te_pred=est.effect(X_test)
te_pred_interval = est.const_marginal_effect_interval(X_test, alpha=0.05)
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Research direction
Start by reproducing the shown LinearDMLCateEstimator workflow, including fit, effect, and const_marginal_effect_interval, with random_state set to 504. Inspect how LinearDMLCateEstimator and RandomForestRegressor handle randomness; done means repeated runs either produce identical results or clearly identify the remaining source of variation.
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Assessment
- Tech stack
- python, scikit-learn
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100