• Corpus ID: 251741575

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

@inproceedings{Singh2020KernelMF,
  title={Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves},
  author={Rahul Singh and Liyuan Xu and Arthur Gretton},
  year={2020}
}

• Rahul Singh, Liyuan Xu, A. Gretton
• Published 10 October 2020
• Computer Science, Mathematics

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.