Model Selection for Multivalued-Treatment Policy Learning in Observational Studies
观察性研究中多值处理政策学习的模型选择
This study investigates the policy learning problem in observational studies, where the treatment variable can be multivalued and the propensity scores are unknown. We approximate the optimal policy in a global policy class with infinite complexity (VC/Natarajan) dimension, using a sequence of sieve policy classes with finite complexity dimension. The optimal policy within each sieve class is estimated by maximizing the empirical welfare, constructed through the doubly…