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.

本文提出了广义 Robinson 分解方法，用于处理结构化干预条件下的条件平均处理效应（CATE）估计问题。在小世界和分子图像的实验中，与之前的工作相比，我们的方法在 CATE 的估计方面表现更好。

结构治疗的因果效应推断

Causal Effect Inference for Structured Treatments

Standard methods in supervised learning separate training and prediction: the
model is fit independently of any test points it may encounter. However, can
knowledge of the next test point $\mathbf{x}_{\star}$ be exploited to improve
prediction accuracy? We address this question in the context of linear
prediction, showing how techniques from semi-parametric inference can be used
transductively to combat regularization bias. We first lower bound the
$\mathbf{x}_{\star}$ prediction error of ridge regression and the Lasso,
showing that they must incur significant bias in certain test directions. We
then provide non-asymptotic upper bounds on the $\mathbf{x}_{\star}$ prediction
error of two transductive prediction rules. We conclude by showing the efficacy
of our methods on both synthetic and real data, highlighting the improvements
single point transductive prediction can provide in settings with distribution
shift.

本文研究如何在标准监督学习之外，利用下一个测试点的信息提高预测准确性，使用半参数推断技术进行推导，提出了两种跨域预测规则对测试点进行预测，并在合成和真实数据上验证了方法的有效性，突出了单点跨越式预测在分布转移设置中的改进作用。