标题： 因果剂量反应函数的估计在数据融合下 显示英文标题

标题： Estimation of causal dose-response functions under data fusion

摘要： 估计因果剂量反应函数具有挑战性，尤其是在单一数据源的数据不足以在所有暴露水平上精确估计响应的情况下。 为了克服这一限制，我们提出了一种数据融合框架，该框架利用与目标分布部分对齐的多个数据源。 具体而言，我们推导了一个Neyman正交损失函数，专门用于数据融合环境下的剂量反应函数估计。 为了提高计算效率，我们提出了一种保持正交性的随机近似方法。 我们使用这种近似方法进行核岭回归，这提供了闭式估计量。 我们的理论分析表明，引入额外的数据源可以得到更紧的有限样本遗憾界和改进的最坏情况性能，这一点通过最小最大下界比较得到了验证。 模拟研究验证了我们方法的实际优势，显示在使用数据融合时估计精度有所提高。 本研究突显了数据融合在估计非光滑参数（如因果剂量反应函数）方面的潜力。 显示英文摘要

摘要： Estimating the causal dose-response function is challenging, particularly when data from a single source are insufficient to estimate responses precisely across all exposure levels. To overcome this limitation, we propose a data fusion framework that leverages multiple data sources that are partially aligned with the target distribution. Specifically, we derive a Neyman-orthogonal loss function tailored for estimating the dose-response function within data fusion settings. To improve computational efficiency, we propose a stochastic approximation that retains orthogonality. We apply kernel ridge regression with this approximation, which provides closed-form estimators. Our theoretical analysis demonstrates that incorporating additional data sources yields tighter finite-sample regret bounds and improved worst-case performance, as confirmed via minimax lower bound comparison. Simulation studies validate the practical advantages of our approach, showing improved estimation accuracy when employing data fusion. This study highlights the potential of data fusion for estimating non-smooth parameters such as causal dose-response functions.