  In the context of individual-level causal inference, we study the problem of
predicting whether someone will respond or not to a treatment based on their
features and past examples of features, treatment indicator (e.g., drug/no
drug), and a binary outcome (e.g., recovery from disease). As a classification
task, the problem is made difficult by not knowing the example outcomes under
the opposite treatment indicators. We assume the effect is monotonic, as in
advertising's effect on a purchase or bail-setting's effect on reappearance in
court: either it would have happened regardless of treatment, not happened
regardless, or happened only depending on exposure to treatment. Predicting
whether the latter is latently the case is our focus. While previous work
focuses on conditional average treatment effect estimation, formulating the
problem as a classification task rather than an estimation task allows us to
develop new tools more suited to this problem. By leveraging monotonicity, we
develop new discriminative and generative algorithms for the
responder-classification problem. We explore and discuss connections to
corrupted data and policy learning. We provide an empirical study with both
synthetic and real datasets to compare these specialized algorithms to standard
benchmarks.
