Online Program Home
  My Program

Abstract Details

Activity Number: 292 - SPEED: Statistics in Epidemiology
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #324177 View Presentation
Title: A Gaussian Process Approach for Estimating Treatment Assignment Mechanisms
Author(s): Brian Vegetabile* and Hal Stern
Companies: University of California, Irvine and University of California, Irvine
Keywords: Bayesian ; Gaussian Processes ; Observational Studies ; Causal Inference ; Nonparametrics ; Propensity Scores
Abstract:

Causal inference in observational studies often relies on estimating an unobserved treatment assignment mechanism and using estimates of treatment assignment probabilities in the analysis of outcomes. This paper assesses the performance of a Bayesian nonparametric framework utilizing Gaussian process priors for the estimation of treatment assignment probabilities as a function of covariates. Gaussian process priors are ideal for several reasons. First, by using a Gaussian process prior the analyst does not have to specify the functional form of the assignment mechanism, avoiding the possibility of model specification bias. Additionally, they can provide insight into the role of the covariates and, in particular, the areas of the covariate space where causal inference is possible. This talk provides simulation results that compare the performance of two different methods of obtaining approximations to the posterior distribution of the treatment assignment mechanism: MCMC sampling of the posterior distribution of the treatment assignment function (and underlying parameters) and an approximation to the posterior distribution derived using expectation propagation.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association