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Activity Number: 220
Type: Topic Contributed
Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
Sponsor: Health Policy Statistics Section
Abstract #311124
Title: Matching to Estimate the Causal Effects from Multiple Treatments
Author(s): Michael Lopez*+ and Roee Gutman
Companies: Brown University and Brown University
Keywords: Causal inference ; Matching ; Propensity score ; Multiple treatments ; Generalized propensity score
Abstract:

The propensity score is a common tool for estimating the causal effect of a binary treatment using observational data. With more than two treatment options, however, propensity score tools require additional assumptions and techniques, the implementations of which have varied across disciplines, including public health and economics. We blend several current methods together, identifying and contrasting the causal effects which each one estimates. Further, we propose a novel matching technique for use with multiple, nominal categorical treatments, which allows for the matching of subjects on a vector of treatment assignment probabilities. Extensive simulations show our algorithm can provide large reductions in the covariates' bias.


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