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.

翻译：我们处理结构化处理(如图表、图像、文本)的有条件平均治疗效果的估计问题,我们建议采用普遍化的鲁滨逊分解法,即(一) 分离因果估计值(减少正规化偏差),(二) 允许一个人插入任意的学习模式,(三) 在轻度假设下拥有准甲骨文汇合保证。 在小世界和分子图的实验中,我们证明我们的方法优于CATE估算的先前工作。