  I congratulate Profs. Binyan Jiang, Rui Song, Jialiang Li, and Donglin Zeng
(JSLZ) for an exciting development in conducting inferences on optimal dynamic
treatment regimes (DTRs) learned via empirical risk minimization using the
entropy loss as a surrogate. JSLZ's approach leverages a
rejection-and-importance-sampling estimate of the value of a given decision
rule based on inverse probability weighting (IPW) and its interpretation as a
weighted (or cost-sensitive) classification. Their use of smooth classification
surrogates enables their careful approach to analyzing asymptotic
distributions. However, even for evaluation purposes, the IPW estimate is
problematic as it leads to weights that discard most of the data and are
extremely variable on whatever remains. In this comment, I discuss an
optimization-based alternative to evaluating DTRs, review several connections,
and suggest directions forward. This extends the balanced policy evaluation
approach of Kallus (2018a) to the longitudinal setting.
