• 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}
}
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… 
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References

SHOWING 1-10 OF 81 REFERENCES

Non‐parametric methods for doubly robust estimation of continuous treatment effects

A novel kernel smoothing approach is developed that requires only mild smoothness assumptions on the effect curve, and still allows for misspecification of either the treatment density or outcome regression.

Targeted Data Adaptive Estimation of the Causal Dose–Response Curve

Abstract Estimation of the causal dose–response curve is an old problem in statistics. In a non-parametric model, if the treatment is continuous, the dose–response curve is not a pathwise

Direct and indirect effects of continuous treatments based on generalized propensity score weighting

This paper proposes semi- and nonparametric methods for disentangling the total causal effect of a continuous treatment on an outcome variable into its natural direct effect and the indirect effect

Extending Marginal Structural Models through Local, Penalized, and Additive Learning

How marginal structural models for causal effects can be extended through the alternative techniques of local, penalized, and additive learning is shown, and nonparametric function estimation methods can be fruitfully applied for making causal inferences.

Kernel Methods for Policy Evaluation: Treatment Effects, Mediation Analysis, and Off-Policy Planning

A novel framework for non-parametric policy evaluation in static and dynamic settings and uses new estimators to evaluate continuous and heterogeneous treatment effects of the US Jobs Corps training program for disadvantaged youth.

Double debiased machine learning nonparametric inference with continuous treatments

We propose a nonparametric inference method for causal effects of continuous treatment variables, under unconfoundedness and in the presence of high-dimensional or nonparametric nuisance parameters.

Estimating Conditional Average Treatment Effects

We consider a functional parameter called the conditional average treatment effect (CATE), designed to capture the heterogeneity of a treatment effect across subpopulations when the unconfoundedness

Estimation of Conditional Average Treatment Effects With High-Dimensional Data

Abstract Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage, the

Uniformly Semiparametric Efficient Estimation of Treatment Effects With a Continuous Treatment

This article studies identification, estimation, and inference of general unconditional treatment effects models with continuous treatment under the ignorability assumption. We show identification of

Debiased Kernel Methods

It is shown that the classic assumptions of RKHS learning theory also imply inference, and it is proved pointwise √ n consistency, Gaussian approximation, and semiparametric efficiency by finite sample arguments.
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