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Questions regarding DRPolicyForest results #874
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If you could provide a concrete reproduction of your issues that might help narrow down any issues, but here are a few thoughts. For question 1, predict_proba gives the fraction of trees in the forest recommending the treatment while predict_value gives the average estimate; usually these would be ranked fairly consistently but that's not necessarily the case. For instance, it's possible that a small number of trees predict a very high value for T2, so that the overall average prediction is higher than that for T1, but the majority of trees predict a higher value for T1, so that gets a higher ranking in predict_proba. For question 2, if you're plotting a single tree from the forest, then it might make sense that that tree assigns treatment 0 to some instance, but that most trees assign some other treatment, so 0 is never recommended by the forest overall. |
Thanks @kbattocchi, (I'll close the issue in a few days in case anyone wants to add some comment ) |
Hi, thanks for the great ci library.
I'm using
DRPolicyForest
and facing some issue.model = DRPolicyForest(...)
Question 1: model's
predict_value()
andpredict_proba()
are returning different rankings between treatments.I thought they return the same rankings (order of magnitude), but they aren't.
How should I interpret this?
Question 2: model's
predict()
method returns zero values for Treatment=0 (control). However, if I draw a plot,model.plot()
, there is a None Treatment(T=0) leaf with numerous samples.Why do they return different results?
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