Nonparametric methods for doubly robust estimation of continuous treatment effects
TLDR
This paper developed a kernel smoothing approach that requires only mild smoothness assumptions on the effect curve and still allows for misspecification of either the treatment density or outcome regression, which is illustrated via simulation and in a study of the effect of nurse staffing on hospital readmissions penalties.Abstract:
Summary
Continuous treatments (e.g. doses) arise often in practice, but many available causal effect estimators are limited by either requiring parametric models for the effect curve, or by not allowing doubly robust covariate adjustment. We develop a novel kernel smoothing approach that requires only mild smoothness assumptions on the effect curve and still allows for misspecification of either the treatment density or outcome regression. We derive asymptotic properties and give a procedure for data-driven bandwidth selection. The methods are illustrated via simulation and in a study of the effect of nurse staffing on hospital readmissions penalties.read more
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References
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Estimating causal effects of treatments in randomized and nonrandomized studies.
TL;DR: A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented in this paper, where the objective is to specify the benefits of randomization in estimating causal effects of treatments.
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Weak Convergence and Empirical Processes: With Applications to Statistics
TL;DR: In this article, the authors define the Ball Sigma-Field and Measurability of Suprema and show that it is possible to achieve convergence almost surely and in probability.
BookDOI
Weak Convergence and Empirical Processes
TL;DR: This chapter discusses Convergence: Weak, Almost Uniform, and in Probability, which focuses on the part of Convergence of the Donsker Property which is concerned with Uniformity and Metrization.
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Local polynomial modelling and its applications
Jianqing Fan,Irène Gijbels +1 more
TL;DR: Applications of Local Polynomial Modeling in Nonlinear Time Series and Automatic Determination of Model Complexity and Framework for Local polynomial regression.
Book
Convergence of stochastic processes
TL;DR: In this paper, the authors define a functional on Stochastic Processes as random functions and propose a uniform convergence of empirical measures in Euclidean spaces, based on the notion of convergence in distribution.