Nonparametric Estimators of Dose-Response Functions: A Simulation Study

• Activity Number: 132
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Michela Bia*+ and Carlos Flores and Alfonso Flores-Lagunes and Alessandra Mattei
• Companies: CEPS/Instead and Orfalea College of Business, Cal Poly and SUNY Binghampton University and University of Florence
• Keywords: Generalized propensity score ; weak unconfoundedness ; kernel estimator ; penalized spline estimator ; dose-response function

Abstract

In this paper we propose three semiparametric estimators of the dose-response function based on kernel and spline techniques. In many observational studies treatment may not be binary or categorical. In such cases, one may be interested in estimating the dose-response function in a setting with a continuous treatment. This approach strongly relies on the uncounfoundedness assumption, which requires the potential outcomes are independent of the treatment conditional on a set of covariates. In this context the generalized propensity score can be used to estimate dose-response functions (DRF) and marginal treatment effect functions. We evaluate the performance of the proposed estimators using Monte Carlo simulation methods. We also apply our approach to the problem of evaluating job training program for disadvantaged youth in the United States (Job Corps program). In this regard, we provide new evidence on the intervention effectiveness by uncovering heterogeneities in the effects of Job Corps training along the different lengths of exposure.