Combining machine learning and propensity score weighting to estimate causal effects in multivalued treatments.
Journal:
Journal of evaluation in clinical practice
Published Date:
Dec 1, 2016
Abstract
RATIONALE, AIMS AND OBJECTIVES: Interventions with multivalued treatments are common in medical and health research; examples include comparing the efficacy of competing interventions and contrasting various doses of a drug. In recent years, there has been growing interest in the development of methods that estimate multivalued treatment effects using observational data. This paper extends a previously described analytic framework for evaluating binary treatments to studies involving multivalued treatments utilizing a machine learning algorithm called optimal discriminant analysis (ODA).