Abstract Details
Activity Number:
|
110
|
Type:
|
Topic Contributed
|
Date/Time:
|
Monday, August 4, 2014 : 8:30 AM to 10:20 AM
|
Sponsor:
|
ENAR
|
Abstract #312998
|
|
Title:
|
High-Dimensional Confounder Reduction for the Estimation of a Causal Quantity
|
Author(s):
|
Mireille Schnitzer*+ and Judith J. Lok and Susan Gruber
|
Companies:
|
University of Montreal and Harvard School of Public Health and Harvard School of Public Health
|
Keywords:
|
causal inference ;
confounder ;
variable selection ;
propensity
|
Abstract:
|
In observational cohorts, estimation of the effectiveness of a non-randomized treatment generally requires the implementation of causal modelling. Often this will involve the estimation of a propensity model, which predicts the probability of taking treatment conditional on all measured suspected confounders. However, in domains like pharmacoepidemiology, this set of hypothetical confounders might be very high-dimensional, while a sufficient adjustment set (that would properly control for confounding bias) might actually be much smaller. In this talk, I will give an overview of the recent literature on causal variable selection, highlight mistakes in common practice, and evaluate different strategies through simulation study and example.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2014 program
|
2014 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Professional Development program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.