title: Optimizing matching and analysis combinations for estimating causal effects.
abstract: Matching methods are common in studies across many disciplines. However, there is limited evidence on how to optimally combine matching with subsequent analysis approaches to minimize bias and maximize efficiency for the quantity of interest. We conducted simulations to compare the performance of a wide variety of matching methods and analysis approaches in terms of bias, variance, and mean squared error (MSE). We then compared these approaches in an applied example of an employment training program. The results indicate that combining full matching with double robust analysis performed best in both the simulations and the applied example, particularly when combined with machine learning estimation methods. To reduce bias, current guidelines advise researchers to select the technique with the best post-matching covariate balance, but this work finds that such an approach does not always minimize mean squared error (MSE). These findings have important implications for future research utilizing matching. To minimize MSE, investigators should consider additional diagnostics, and use of simulations tailored to the study of interest to identify the optimal matching and analysis combination.
authors.affiliation: Division of Epidemiology, University of California- Berkeley School of Public Health, 50 University Hall, Berkeley, CA 94720-7360, USA.
authors.forename: K Ellicott
authors.initials: KE
authors.lastname: Colson
authors.affiliation: Division of Epidemiology, University of California- Berkeley School of Public Health, 50 University Hall, Berkeley, CA 94720-7360, USA.|Center for Health and Community, University of California- San Francisco, 3333 California St, Suite 465, San Francisco, CA 94143-0844, USA.
authors.forename: Kara E
authors.initials: KE
authors.lastname: Rudolph
authors.affiliation: Division of Epidemiology, University of California- Berkeley School of Public Health, 50 University Hall, Berkeley, CA 94720-7360, USA.
authors.forename: Scott C
authors.initials: SC
authors.lastname: Zimmerman
authors.affiliation: Division of Epidemiology, University of California- Berkeley School of Public Health, 50 University Hall, Berkeley, CA 94720-7360, USA.
authors.forename: Dana E
authors.initials: DE
authors.lastname: Goin
authors.affiliation: Departments of Mental Health, Biostatistics, and Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, 624 N Broadway, Baltimore, MD 21205, USA.
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Stuart
authors.affiliation: Division of Biostatistics, University of California- Berkeley School of Public Health, 101 Haviland Hall, Berkeley, CA 94720-7358, USA.
authors.forename: Mark van der
authors.initials: Mv
authors.lastname: Laan
authors.affiliation: Division of Epidemiology, University of California- Berkeley School of Public Health, 50 University Hall, Berkeley, CA 94720-7360, USA.
authors.forename: Jennifer
authors.initials: J
authors.lastname: Ahern
mesh_terms: D015982:Bias; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004493:Education; D004651:Employment; D006801:Humans; D015233:Models, Statistical; D057216:Propensity Score; D058996:Quality Improvement