Why can doubly robust off-policy evaluation outperform importance sampling?

A doubly robust off-policy estimator combines importance ratios with an estimated action-value model. Under the method's standard support and logging-policy assumptions, which description is most accurate?

It guarantees lower mean-squared error than every importance-sampling estimator for every finite dataset

It is unbiased only when both the value model and importance ratios are exact

It uses the value model as a baseline and an importance-weighted residual correction, potentially retaining unbiasedness with lower variance

It eliminates importance ratios whenever the value model has lower validation error than the behaviour model