PSweight: An R Package for Propensity Score Weighting Analysis

Abstract

Propensity score weighting is an important tool for comparative effectiveness research.Besides the inverse probability of treatment weights (IPW), recent development has introduced a general class of balancing weights, corresponding to alternative target populations and estimands. In particular, the overlap weights (OW) lead to optimal covariate balance and estimation efficiency, and a target population of scientific and policy interest. We develop the R package PSweight to provide a comprehensive design and analysis platform for causal inference based on propensity score weighting. PSweight supports (i) a variety of balancing weights, (ii) binary and multiple treatments,(iii) simple and augmented weighting estimators, (iv) nuisance-adjusted sandwich variances, and(v) ratio estimands. PSweight also provides diagnostic tables and graphs for covariate balance assessment. We demonstrate the functionality of the package using a data example from the NationalChild Development Survey (NCDS), where we evaluate the causal effect of educational attainment on income.

arXiv comment18 pages, 3 figures, 5 tables

References in corpus (4)

• Propensity Score Matching and Subclassification in Observational Studies with Multi-level Treatments
• Propensity score weighting under limited overlap and model misspecification
• Propensity Score Weighting for Causal Subgroup Analysis
• Propensity Score Weighting for Covariate Adjustment in Randomized Clinical Trials

Cited by in corpus (1)

• Covariate balancing for causal inference on categorical and continuous treatments