title: Combining machine learning and propensity score weighting to estimate causal effects in multivalued treatments.
abstract: OBJECTIVE
  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).
  
  
  METHODS
  We describe the differences between regression-based treatment effect estimators and effects estimated using the ODA framework. We then present an empirical example using data from an intervention including three study groups to compare corresponding effects.
  
  
  RESULTS
  The regression-based estimators produced statistically significant mean differences between the two intervention groups, and between one of the treatment groups and controls. In contrast, ODA was unable to discriminate between distributions of any of the three study groups.
  
  
  CONCLUSIONS
  Optimal discriminant analysis offers an appealing alternative to conventional regression-based models for estimating effects in multivalued treatment studies because of its insensitivity to skewed data and use of accuracy measures applicable to all prognostic analyses. If these analytic approaches produce consistent treatment effect P values, this bolsters confidence in the validity of the results. If the approaches produce conflicting treatment effect P values, as they do in our empirical example, the investigator should consider the ODA-derived estimates to be most robust, given that ODA uses permutation P values that require no distributional assumptions and are thus, always valid.
authors.affiliation: Linden Consulting Group, LLC, Ann Arbor, Michigan, USA.|Division of General Medicine, Medical School - University of Michigan, Ann Arbor, Michigan, USA.
authors.forename: Ariel
authors.initials: A
authors.lastname: Linden
authors.affiliation: Optimal Data Analysis, LLC, Evanston, Illinois, USA.
authors.forename: Paul R
authors.initials: PR
authors.lastname: Yarnold
mesh_terms: D000368:Aged; D000465:Algorithms; D015984:Causality; D036262:Empirical Research; D005260:Female; D006801:Humans; D000069550:Machine Learning / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D019370:Observation; D057216:Propensity Score; D012044:Regression Analysis; D016896:Treatment Outcome
keywords: causal inference; doubly robust; inverse probability of treatment weighting; machine learning; marginal mean weighting through stratification; multivalued treatments; observational studies; propensity score