simulation

High-dimensional Q-learning for Dynamic Treatment Regimes

Prof. Rui Song, Department of Statistics, North Carolina State University

Mar 31, 12:00 - 13:00

B3 L5 R5220

HQ-learning statistical inference data analysis simulation

Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients’ responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of nonregularity problems in the presence of nonrespondents who have zero-treatment effects, especially when the dimension of the tailoring variables is high.