Data Poisoning Attacks on Off-Policy Policy Evaluation Methods

arXiv:2404.04714v1 Announce Type: cross
Abstract: Off-policy Evaluation (OPE) methods are a crucial tool for evaluating policies in high-stakes domains such as healthcare, where exploration is often infeasible, unethical, or expensive. However, the extent to which such methods can be trusted under adversarial threats to data quality is largely unexplored. In this work, we make the first attempt at investigating the sensitivity of OPE methods to marginal adversarial perturbations to the data. We design a generic data poisoning attack framework leveraging …

adversarial arxiv attacks can cs.ai cs.cr cs.lg data data poisoning data quality domains evaluation exploration healthcare high poisoning poisoning attacks policies policy quality threats tool under work