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When decision-makers can directly intervene, policy evaluation algorithmsgive valid causal estimates. In off-policy evaluation (OPE), there may existunobserved variables that both impact the dynamics and are used by the unknownbehavior policy. These "confounders" will introduce spurious correlations andnaive estimates for a new policy will be biased. We develop worst-case boundsto assess sensitivity to these unobserved confounders in finite horizons whenconfounders are drawn iid each period. We demonstrate that a model-basedapproach with robust MDPs gives sharper lower bounds by exploiting domainknowledge about the dynamics. Finally, we show that when unobserved confoundersare persistent over time, OPE is far more difficult and existing techniquesproduce extremely conservative bounds.

Model-Free and Model-Based Policy Evaluation when Causality is Uncertain