Title: Adaptive confidence intervals for regression coefficients in Q-learning

Authors:  Eric Laber & Susan Murphy

Abstract: Dynamic treatment regimes (or treatment policies) are used
to operationalize
multi-stage decision making in the medical field. Common approaches to
constructing the dynamic treatment regimes from data, such as Q-Learning,
employ non-smooth functionals of the data. The non-smoothness leads to
non-regular asymptotics under certain generative models. Methods that ignore
the non-regularity have poor performance in small samples. In this
talk, we propose a bootstrap based method for constructing
asymptotically valid
confidence sets. This method is adaptive in the sense that it provides exact
coverage when the true underlying generative model leads to regular
asymptotics and is conservative otherwise. Empirical studies show that the
amount of conservatism is small.