AIPW: An R Package for Augmented Inverse Probability-Weighted Estimation of Average Causal Effects.

ABSTRACT: An increasing number of recent studies have suggested that doubly robust estimators with cross-fitting should be used when estimating causal effects with machine learning methods. However, not all existing programs that implement doubly robust estimators support machine learning methods and cross-fitting, or provide estimates on multiplicative scales. To address these needs, we developed AIPW, a software package implementing augmented inverse probability weighting (AIPW) estimation of average causal effects in R (R Foundation for Statistical Computing, Vienna, Austria). Key features of the AIPW package include cross-fitting and flexible covariate adjustment for observational studies and randomized controlled trials (RCTs). In this paper, we use a simulated RCT to illustrate implementation

SUBMITTER: Zhong Y 

PROVIDER: S-EPMC8796813 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature