Before revision:  This paper provides a DR estimator for dependent samples obtained in adaptive experimentsand introduces two related topics. First, we propose adaptive-fitting as a variant of sample-splitting for showing an asymptotically normal semiparametric estimator from dependent samples without non-Donsker nuisance estimators . Second, we report an empirical paradox that a DR estimator shows better performances than other estimators using the true logging policy instead of its estimator .
After revision:  This paper provides a DR estimator for dependent samples obtained from adaptive experiments. To obtain an asymptotically normal semiparametric estimator from dependent samples without non-Donsker nuisance estimators . Second, we report an empirical paradox that a DR estimator shows better performances than other estimators using the true logging policy instead of its estimator .