1. AIPW: An R Package for Augmented Inverse Probability–Weighted Estimation of Average Causal Effects.
- Author
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Zhong, Yongqi, Kennedy, Edward H, Bodnar, Lisa M, and Naimi, Ashley I
- Subjects
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STATISTICS , *EXPERIMENTAL design , *MACHINE learning , *SOFTWARE architecture , *HUMAN services programs , *ATTRIBUTION (Social psychology) , *DATA analysis , *STATISTICAL models , *ALGORITHMS , *EPIDEMIOLOGICAL research - 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 of the AIPW estimator. We also perform a simulation study to evaluate the performance of the AIPW package compared with other doubly robust implementations, including CausalGAM , npcausal , tmle , and tmle3. Our simulation showed that the AIPW package yields performance comparable to that of other programs. Furthermore, we also found that cross-fitting substantively decreases the bias and improves the confidence interval coverage for doubly robust estimators fitted with machine learning algorithms. Our findings suggest that the AIPW package can be a useful tool for estimating average causal effects with machine learning methods in RCTs and observational studies. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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