Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves

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 improved 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. In nonlinear simulations with many covariates, we achieve state-of-the-art performance.