In this work, we present a novel way of computing IPS using a position-bias model for deterministic logging policies. This technique significantly widens the policies on which OPE can be used. We validate this technique using two different experiments on industry-scale data. The OPE results are clearly strongly correlated with the online results, with some constant bias. The estimator requires the examination model to be a reasonably accurate approximation of real user behaviour.
Cite
@article{arxiv.2208.14980,
title = {Inverse Propensity Score based offline estimator for deterministic ranking lists using position bias},
author = {Nick Wood and Sumit Sidana},
journal= {arXiv preprint arXiv:2208.14980},
year = {2022}
}