Open AccessUSING MULTIVARIATE MATCHED SAMPLING AND REGRESSION ADJUSTMENT TO CONTROL BIAS IN OBSERVATIONAL STUDIES
Author(s) -
Rubin Donald B.
Publication year - 1978
Publication title -
ets research bulletin series
Language(s) - English
Resource type - Journals
eISSN - 2333-8504
pISSN - 0424-6144
DOI - 10.1002/j.2333-8504.1978.tb01163.x
Subject(s) - multivariate statistics , mahalanobis distance , statistics , bayesian multivariate linear regression , observational study , metric (unit) , matching (statistics) , propensity score matching , mathematics , regression , regression analysis , econometrics , engineering , operations management
Monte Carlo methods are used to study the efficacy of multivariate matched sampling and regression adjustment for controlling bias due to specific matching variables when dependent variables are moderately nonlinear in . The general conclusion is that nearest available Mahalanobis metric matching in combination with regression adjustment on matched pair differences is a highly effective plan for controlling bias due to .