All your data are always missing: incorporating bias due to measurement error into the potential outcomes framework.
Epidemiologists often use the potential outcomes framework to cast causal inference as a missing data problem. Here, we demonstrate how bias due to measurement error can be described in terms of potential outcomes and considered in concert with bias from other sources. In addition, we illustrate how acknowledging the uncertainty that arises due to measurement error increases the amount of missing information in causal inference. We use a simple example to show that estimating the average treatment effect requires the investigator to perform a series of hidden imputations based on strong assumptions.
Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA jessedwards@unc.edu.
Jessie K
JK
Edwards
Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Stephen R
SR
Cole
Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Daniel
D
Westreich
D015982:Bias; D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D012107:Research Design / Q000592:standards*
Bias (Epidemiology); HIV; causal inference; missing data