[P] Boosted Off-policy Learning

Hello! I'm implementing [this](https://arxiv.org/abs/2208.01148) paper (Boosted Off-policy learning) and I've come to a technical or conceptual halt.

Ive come to this step: The authors frame the contextual bandit problem as a boosted weighted regression with surrogate labels and weights:

https://preview.redd.it/26ppb3k58atc1.png?width=1310&format=png&auto=webp&s=258b58a4b62e07918f97deee1ddea79ebd4abcf4

I do have a dataset (x\_i, a\_i, r\_i, p\_i), so I can easily generate the w\_i and pass them to xgboost, but how do I generate the y\_i's if I cant access the "previous" trees prediction? (the pi\_t is a …

authors dataset generate ive labels machinelearning prediction regression them trees xgboost