Augmented Inverse Probability Weighting
Augmented Inverse Probability Weighting Base Class (AIPW_base)
Augmented Inverse Probability Weighting (AIPW) uses tmle or tmle3 as i...
Augmented Inverse Probability Weighting (AIPW) uses tmle or tmle3 as i...
AIPW wrapper function
Augmented Inverse Probability Weighting (AIPW)
Fit the data to the AIPW object
Plot the inverse probability weights using truncated propensity scores...
Plot the propensity scores by exposure status
Repeated Crossfitting Procedure for AIPW
Fit the data to the AIPW object repeatedly
Fit the data to the AIPW object stratified by A for the outcome mode...
Summary of the repeated_estimates from repfit() in the Repeated ob...
Summary of the average treatment effects from AIPW
The 'AIPW' package implements the augmented inverse probability weighting, a doubly robust estimator, for average causal effect estimation with user-defined stacked machine learning algorithms. To cite the 'AIPW' package, please use: "Yongqi Zhong, Edward H. Kennedy, Lisa M. Bodnar, Ashley I. Naimi (2021). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology. <doi:10.1093/aje/kwab207>". Visit: <https://yqzhong7.github.io/AIPW/> for more information.