Continuous treatments (e.g., doses) arise often in practice, but many
available causal effect estimators are limited by either requiring parametric
models for the effect curve, or by not allowing doubly robust covariate
adjustment. We develop a novel kernel smoothing approach that requires only
mild smoothness assumptions on the effect curve, and still allows for
misspecification of either the treatment density or outcome regression. We
derive asymptotic properties and give a procedure for data-driven bandwidth
selection. The methods are illustrated via simulation and in a study of the
effect of nurse staffing on hospital readmissions penalties.

本文提出一种新的核平滑方法，可以在不需要参数模型的情况下，对连续治疗（如剂量）进行因果效应估计，并允许处理密度或结果回归的错误规范。方法经过了模拟证明，并应用于护士人员配备对医院复诊惩罚的研究中。

双重可靠估计连续处理效应的非参数方法

Nonparametric methods for doubly robust estimation of continuous  treatment effects

We propose and analyze a novel framework for learning sparse representations,
based on two statistical techniques: kernel smoothing and marginal regression.
The proposed approach provides a flexible framework for incorporating feature
similarity or temporal information present in data sets, via non-parametric
kernel smoothing. We provide generalization bounds for dictionary learning
using smooth sparse coding and show how the sample complexity depends on the L1
norm of kernel function used. Furthermore, we propose using marginal regression
for obtaining sparse codes, which significantly improves the speed and allows
one to scale to large dictionary sizes easily. We demonstrate the advantages of
the proposed approach, both in terms of accuracy and speed by extensive
experimentation on several real data sets. In addition, we demonstrate how the
proposed approach could be used for improving semi-supervised sparse coding.

本文提出并分析了一种基于核平滑和边缘回归的新型稀疏表示学习框架，该方法提供了一个灵活的框架，通过非参数核平滑技术，将特征相似性或时间信息纳入数据集中，这对于稀疏编码、半监督稀疏编码等任务具有重要作用。我们提出了 Smooth Sparse Coding 的广义界限，揭示了样本复杂度取决于所使用的核函数的 L1 范数。此外，我们提高了获得稀疏码的速度，并允许扩展到大规模字典大小的具体操作，我们在数个真实数据集上进行了大量实验证明了所提出的方法的优点。