title: Improving causal inference with a doubly robust estimator that combines propensity score stratification and weighting.
abstract: OBJECTIVE
  When a randomized controlled trial is not feasible, health researchers typically use observational data and rely on statistical methods to adjust for confounding when estimating treatment effects. These methods generally fall into 3 categories: (1) estimators based on a model for the outcome using conventional regression adjustment; (2) weighted estimators based on the propensity score (ie, a model for the treatment assignment); and (3) "doubly robust" (DR) estimators that model both the outcome and propensity score within the same framework. In this paper, we introduce a new DR estimator that utilizes marginal mean weighting through stratification (MMWS) as the basis for weighted adjustment. This estimator may prove more accurate than treatment effect estimators because MMWS has been shown to be more accurate than other models when the propensity score is misspecified. We therefore compare the performance of this new estimator to other commonly used treatment effects estimators.
  
  
  METHODS
  Monte Carlo simulation is used to compare the DR-MMWS estimator to regression adjustment, 2 weighted estimators based on the propensity score and 2 other DR methods. To assess performance under varied conditions, we vary the level of misspecification of the propensity score model as well as misspecify the outcome model.
  
  
  RESULTS
  Overall, DR estimators generally outperform methods that model one or the other components (eg, propensity score or outcome). The DR-MMWS estimator outperforms all other estimators when both the propensity score and outcome models are misspecified and performs equally as well as other DR estimators when only the propensity score is misspecified.
  
  
  CONCLUSIONS
  Health researchers should consider using DR-MMWS as the principal evaluation strategy in observational studies, as this estimator appears to outperform other estimators in its class.
authors.affiliation: Linden Consulting Group, LLC, Ann Arbor, MI, USA.|Division of General Medicine, Medical School, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Ariel
authors.identifier: http://orcid.org/0000-0002-0755-2313
authors.initials: A
authors.lastname: Linden
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D064887:Observational Studies as Topic / Q000379:methods*; D057216:Propensity Score
keywords: causal inference; doubly robust; inverse probability of treatment weights; marginal mean weighting through stratification; propensity score; stratification; treatment effects