Authors: Daniel Guzman-Olivares, Philipp Schmidt, Jacek Golebiowski, Artur Bekasov
Abstract: Off-policy evaluation can leverage logged data to estimate the effectiveness
of new policies in e-commerce, search engines, media streaming services, or
automatic diagnostic tools in healthcare. However, the performance of baseline
off-policy estimators like IPS deteriorates when the logging policy
significantly differs from the evaluation policy. Recent work proposes sharing
information across similar actions to mitigate this problem. In this work, we
propose an alternative estimator that shares information across similar
contexts using clustering. We study the theoretical properties of the proposed
estimator, characterizing its bias and variance under different conditions. We
also compare the performance of the proposed estimator and existing approaches
in various synthetic problems, as well as a real-world recommendation dataset.
Our experimental results confirm that clustering contexts improves estimation
accuracy, especially in deficient information settings.
Source: http://arxiv.org/abs/2502.21304v1
