Propensity Score Weighting for Causal Inference with Multiple Treatments

Understand

Causal or unconfounded descriptive comparisons between multiple groups are common in observational studies.

• Motivated from a racial disparity study in health services research, we propose a unified propensity score weighting framework, the balancing weights, for estimating causal effects with multiple treatments.
• These weights incorporate the generalized propensity scores to balance the weighted covariate distribution of each treatment group, all weighted toward a common pre-specified target population.
• The class of balancing weights include several existing approaches such as the inverse probability weights and trimming weights as special cases.