  Despite notable results in various fields over the recent years, deep
reinforcement learning (DRL) algorithms lack transparency, affecting user trust
and hindering their deployment to high-risk tasks. Causal confusion refers to a
phenomenon where an agent learns spurious correlations between features which
might not hold across the entire state space, preventing safe deployment to
real tasks where such correlations might be broken. In this work, we examine
whether an agent relies on spurious correlations in critical states, and
propose an alternative subset of features on which it should base its decisions
instead, to make it less susceptible to causal confusion. Our goal is to
increase transparency of DRL agents by exposing the influence of learned
spurious correlations on its decisions, and offering advice to developers about
feature selection in different parts of state space, to avoid causal confusion.
We propose ReCCoVER, an algorithm which detects causal confusion in agent's
reasoning before deployment, by executing its policy in alternative
environments where certain correlations between features do not hold. We
demonstrate our approach in taxi and grid world environments, where ReCCoVER
detects states in which an agent relies on spurious correlations and offers a
set of features that should be considered instead.
