  We address the estimation of conditional average treatment effects (CATEs)
for structured treatments (e.g., graphs, images, texts). Given a weak condition
on the effect, we propose the generalized Robinson decomposition, which (i)
isolates the causal estimand (reducing regularization bias), (ii) allows one to
plug in arbitrary models for learning, and (iii) possesses a quasi-oracle
convergence guarantee under mild assumptions. In experiments with small-world
and molecular graphs we demonstrate that our approach outperforms prior work in
CATE estimation.
