  When decision-makers can directly intervene, policy evaluation algorithms
give valid causal estimates. In off-policy evaluation (OPE), there may exist
unobserved variables that both impact the dynamics and are used by the unknown
behavior policy. These "confounders" will introduce spurious correlations and
naive estimates for a new policy will be biased. We develop worst-case bounds
to assess sensitivity to these unobserved confounders in finite horizons when
confounders are drawn iid each period. We demonstrate that a model-based
approach with robust MDPs gives sharper lower bounds by exploiting domain
knowledge about the dynamics. Finally, we show that when unobserved confounders
are persistent over time, OPE is far more difficult and existing techniques
produce extremely conservative bounds.
