Abstract Details
Activity Number:
|
491
|
Type:
|
Contributed
|
Date/Time:
|
Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Statistics in Epidemiology
|
Abstract - #310272 |
Title:
|
M-Bias and Butterfly-Bias in the Gaussian Linear Structural Equation Models
|
Author(s):
|
Peng Ding*+ and Luke Miratrix
|
Companies:
|
Harvard University and Harvard University
|
Keywords:
|
Causality ;
Collider ;
Directed acyclic graph ;
Stratification ;
V structure
|
Abstract:
|
``M-Bias'', as it is called in the epidemiological literature, is the bias introduced by conditioning on a pretreatment variable due to a particular ``M-structure'' between two unobserved variables and an observed treatment, outcome, and collider. This potential source of bias, which can occur even when the treatment and the outcome are not confounded, has been the source of considerable controversy. We show the magnitude of the M-Bias in Gaussian linear structural equation models is relatively small compared to confounding bias, suggesting that it would generally not be a serious concern in applied settings. These formulae allow for identifying under which circumstances bias is inflated or reduced. Our theoretic results are consistent with recent empirical findings from simulation studies. We also generalize the M-bias setting to allow for the correlation between the latent factors to be nonzero, and to allow for the collider to also be an actual confounder between the treatment and the outcome. Deviations from the M-Bias structure change the level of bias, shedding light on whether we should condition on a given pretreatment covariate or not.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education 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.