Nonparametric methods for doubly robust estimation of continuous treatment effects

TL;DR

This paper introduces a new kernel smoothing method for estimating continuous treatment effects that is doubly robust and requires minimal assumptions, improving flexibility and reliability in causal inference.

Contribution

It develops a nonparametric, doubly robust estimator for continuous treatments that handles model misspecification and includes a data-driven bandwidth selection procedure.

Findings

Method performs well in simulations.

Applied to nurse staffing data, revealing treatment effects.

Estimator is robust to certain model misspecifications.

Abstract

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.