标题： 正交统计学习与自协调损失 显示英文标题

标题： Orthogonal Statistical Learning with Self-Concordant Loss

摘要： 正交统计学习和双机器学习已成为存在干扰项的情况下两阶段统计预测的一般框架。我们建立了满足自协调性质的损失函数的正交统计学习方法的超额风险的非渐近界，这些界在提升维度因子的同时去除了强凸假设。我们通过多个治疗效果估计和广义部分线性建模的例子来说明结果。 显示英文摘要

摘要： Orthogonal statistical learning and double machine learning have emerged as general frameworks for two-stage statistical prediction in the presence of a nuisance component. We establish non-asymptotic bounds on the excess risk of orthogonal statistical learning methods with a loss function satisfying a self-concordance property. Our bounds improve upon existing bounds by a dimension factor while lifting the assumption of strong convexity. We illustrate the results with examples from multiple treatment effect estimation and generalized partially linear modeling.