Matching Estimators for Causal Effects of Multiple Treatments

• Activity Number: 72 - Methods for Causal and Integrative Analysis in Health Studies
• Type: Contributed
• Date/Time: Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Anthony Scotina* and Roee Gutman
• Companies: Brown University and Brown University
• Keywords: Causal inference ; Matching ; Multiple treatments ; Generalized propensity score ; Observational data

Abstract

Matching estimators for average treatment effects are widely used in the binary treatment setting, in which missing potential outcomes are imputed as the average of observed outcomes of all matches for each unit. With more than two treatment groups, however, estimation using matching requires additional techniques. In this paper, we propose a nearest-neighbors matching estimator for use with multiple, nominal treatments, and use simulations to show that this method is precise and has coverage levels that are close to nominal.