PSweight: An R Package for Propensity Score Weighting Analysis

TL;DR

The paper introduces PSweight, an R package that facilitates propensity score weighting for causal inference, supporting various weights, treatments, estimators, and diagnostics, demonstrated through a real data example.

Contribution

The paper presents a comprehensive R package, PSweight, enabling flexible propensity score weighting analysis with multiple features and diagnostics for causal inference.

Findings

Supports diverse weighting methods including overlap weights

Handles binary and multiple treatments

Provides diagnostic tools for covariate balance

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.