Kernel methods for causal functions: dose, heterogeneous and incremental response curves
基于核岭回归提出非参数因果函数（如剂量、异质性和增量响应曲线）的估计量，具有简单闭式解和一致收敛性，适用于离散或连续处理变量与协变量，并在美国Job Corps培训项目政策评估中验证了性能。
Abstract We propose estimators based on kernel ridge regression for nonparametric causal functions such as dose, heterogeneous and incremental response curves. The treatment and covariates may be discrete or continuous in general spaces. Because of a decomposition property specific to the reproducing kernel Hilbert space, our estimators have simple closed-form solutions. We prove uniform consistency with finite sample rates via an 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 programme for disadvantaged youths.