We investigate the problem of offline policy learning in the presence of unobserved confounders, which may arise in both observational studies and adaptive experiments (e.g., self-selection and noncompliance in sequential medical settings). In particular, we study this problem under the f -sensitivity model, which characterizes the confounding effect by its “average” strength. Under the f -sensitivity model, we characterize the distribution shift from the observable to the counterfactual and design a distributionally robust policy learning algorithm, f -SR(ad)L, which maximizes the expected outcome within a given policy class Π . We show that the sub-optimality gap of f -SR(ad)L learned from a sequential (i.i.d. or adaptively collected) dataset is of the order O(κ(Π)n​), where κ(Π) is the entropy integral of Π under the Hamming distance and n is the sample size. A matching lower bound of is provided to show the optimality of the rate. Finally, we assess our method on synthetic and a real-world data on lung cancer treatments to demonstrate its advantage over existing benchmarks.