Original paper
AIPW: An R Package for Augmented Inverse Probability–Weighted Estimation of Average Causal Effects
Volume: 190, Issue: 12, Pages: 2690 - 2699
Published: Jul 14, 2021
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...
Paper Details
Title
AIPW: An R Package for Augmented Inverse Probability–Weighted Estimation of Average Causal Effects
Published Date
Jul 14, 2021
Volume
190
Issue
12
Pages
2690 - 2699