  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 influence functions from robust
statistics to carefully construct perturbations that maximize error in the
policy value estimates. We carry out extensive experimentation with multiple
healthcare and control datasets. Our results demonstrate that many existing OPE
methods are highly prone to generating value estimates with large errors when
subject to data poisoning attacks, even for small adversarial perturbations.
These findings question the reliability of policy values derived using OPE
methods and motivate the need for developing OPE methods that are statistically
robust to train-time data poisoning attacks.
