  I propose kernel ridge regression estimators for long term causal inference,
where a short term experimental data set containing randomized treatment and
short term surrogates is fused with a long term observational data set
containing short term surrogates and long term outcomes. I propose estimators
of treatment effects, dose responses, and counterfactual distributions with
closed form solutions in terms of kernel matrix operations. I allow covariates,
treatment, and surrogates to be discrete or continuous, and low, high, or
infinite dimensional. For long term treatment effects, I prove $\sqrt{n}$
consistency, Gaussian approximation, and semiparametric efficiency. For long
term dose responses, I prove uniform consistency with finite sample rates. For
long term counterfactual distributions, I prove convergence in distribution.
