We study semiparametric estimation of average treatment effects in a structured outcome model whose mean function is indexed by a finite-dimensional parameter, while the additive error distribution is left otherwise unspecified apart from mild regularity conditions and independence from treatment and baseline covariates. The framework is motivated by policy-evaluation settings in which the main economic structure is plausibly low dimensional but outcome distributions are distinctly non-Gaussian, for example because earnings are skewed or heavy tailed. We derive the efficient influence function and semiparametric efficiency bound for the average treatment effect under this model, and we show how the resulting estimator can be implemented through a cross-fitted targeted updating step driven by the efficient regression score. Simulation evidence indicates that when the mean structure is correctly specified and the main difficulty lies in the error distribution, the proposed estimator can deliver smaller root mean squared error and shorter confidence intervals than Gaussian working-model inference, Bayesian additive regression trees, and augmented inverse-probability weighting under more imbalanced treatment assignment. An application to the National Supported Work program illustrates the empirical relevance of the approach for transformed earnings outcomes.

翻译：我们研究在结构型结果模型中进行平均处理效应的半参数估计，该模型的均值函数由有限维参数索引，而加性误差分布除满足轻度正则条件及与处理变量和基线协变量独立外，其余部分均未指定。该框架受政策评估场景驱动，其中主要经济结构具有合理的低维特性，但结果分布呈现显著的非高斯性，例如因收入偏态分布或重尾特征所致。我们在此模型下推导了平均处理效应的有效影响函数与半参数效率界，并展示了如何通过由有效回归得分驱动的交叉拟合目标更新步骤实现该估计量。模拟证据表明：当均值结构被正确设定且主要困难在于误差分布时，与高斯工作模型推断、贝叶斯加性回归树及处理分配更不平衡下的增强型逆概率加权法相比，所提估计量能实现更小的均方根误差和更短的置信区间。针对国家支持工作计划的应用展示了该方法在处理转换后收入结果时的实证相关性。