论文标题
因果功能的内核方法：剂量，异质和增量响应曲线
Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves
论文作者
论文摘要
我们提出了基于内核脊回归的估计量，以用于非参数因果功能，例如剂量，异质和增量响应曲线。在一般空间中，治疗和协变量可能是离散的或连续的。由于特定于RKHS的分解属性，我们的估计器具有简单的封闭形式解决方案。通过对广义核脊回归的原始分析，我们证明了与有限采样率的一致性。我们将主要结果扩展到反事实分布和前门标准确定的因果功能。我们在许多协变量的非线性模拟中实现了最先进的表现，并对美国弱势青年的求职军团培训计划进行了政策评估。
We propose estimators based on kernel ridge regression for nonparametric causal functions such as dose, heterogeneous, and incremental response curves. Treatment and covariates may be discrete or continuous in general spaces. Due to a decomposition property specific to the RKHS, our estimators have simple closed form solutions. We prove uniform consistency with finite sample rates via original analysis of generalized kernel ridge regression. We extend our main results to counterfactual distributions and to causal functions identified by front and back door criteria. We achieve state-of-the-art performance in nonlinear simulations with many covariates, and conduct a policy evaluation of the US Job Corps training program for disadvantaged youths.
