We propose a novel framework for non-parametric policy evaluationin static and dynamic settings. Under the assumption of selection on observables, we consider treatment effects of the population, of sub-populations , and of alternative populations that may have alternative covariate distributions. We further consider the decomposition of a total effect into a direct effect and an indirect effect ( as mediated by a particular mechanism) . Under the assumption of sequential selection on observables, we consider the effects of sequences of treatments. Across settings, we allow for treatments that may be discrete , continuous, or even text. Across settings, we allow for estimation of not only counterfactual mean outcomes but also counterfactual distributions of outcomes. We unify analyses across settings by showing that all of these causal learning problems reduce to the re-weighting of a prediction, i.e. causal adjustment. We implement the re-weighting as an inner product in a function space called a reproducing kernel Hilbert space (RKHS), with a  closed form solution that can be computed in one line of code. We prove uniform consistency and provide finite sample rates of convergence.  We evaluate our estimators in simulations devised by other authors. We use our new estimators to evaluate continuousand heterogeneous treatment effects of the US Jobs Corps training program for disadvantaged youth.
We propose a family of reproducing kernel ridge estimators for nonparametric and semiparametric policy evaluation. The framework includes (i) treatment effects of the population, of subpopulations , and of alternative populations ; (ii) the decomposition of a total effect into a direct effect and an indirect effect ( mediated by a particular mechanism) ; and (iii) effects of sequences of treatments. Treatment and covariates may be discrete or continuous, and low, high, or infinite dimensional. We consider estimation of means, increments, and distributions of counterfactual outcomes. Each estimator is an inner product in a reproducing kernel Hilbert space (RKHS), with a one line, closed form solution . For the nonparametric case, we prove uniform consistency and provide finite sample rates of convergence. For the semiparametric case, we prove root n consistency, Gaussian approximation, and semiparametric efficiency by finite sample arguments. We evaluate our estimators in simulations then estimate continuous, heterogeneous, incremental, and mediated treatment effects of the US Jobs Corps training program for disadvantaged youth.
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novel framework for non-parametric policy evaluationin static and dynamic settings. Under the assumption of selection on observables, we consider
family of reproducing kernel ridge estimators for nonparametric and semiparametric policy evaluation. The framework includes (i)
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sub-populations
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that may have alternative covariate distributions. We further consider
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as
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Across settings, we allow for treatments that
Treatment and covariates
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, continuous, or even text. Across settings, we allow for estimation of not only counterfactual mean outcomes but also counterfactual distributions of outcomes. We unify analyses across settings by showing that all of these causal learning problems reduce to the re-weighting of a prediction, i.e. causal adjustment. We implement the re-weighting as
or continuous, and low, high, or infinite dimensional. We consider estimation of means, increments, and distributions of counterfactual outcomes. Each estimator is
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function space called a
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one line,
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that can be computed in one line of code. We
. For the nonparametric case, we
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For the semiparametric case, we prove root n consistency, Gaussian approximation, and semiparametric efficiency by finite sample arguments.
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devised by other authors. We use our new estimators to evaluate continuousand heterogeneous
then estimate continuous, heterogeneous, incremental, and mediated