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AIPW

Augmented Inverse Probability Weighting

The 'AIPW' pacakge 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, In Press). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology". Visit: <https://yqzhong7.github.io/AIPW/> for more information.

Functions (12)

AIPW

Augmented Inverse Probability Weighting (AIPW)

AIPW_base

Augmented Inverse Probability Weighting Base Class (AIPW_base)

AIPW_nuis

Augmented Inverse Probability Weighting (AIPW) uses tmle or tmle3 as inputs

AIPW_tmle

Augmented Inverse Probability Weighting (AIPW) uses tmle or tmle3 as inputs

aipw_wrapper

AIPW wrapper function

eager_sim_obs

Simulated Observational Study

eager_sim_rct

Simulated Randomized Trial

fit

Fit the data to the AIPW object

plot.ip_weights

Plot the inverse probability weights using truncated propensity scores by exposure status

plot.p_score

Plot the propensity scores by exposure status

stratified_fit

Fit the data to the AIPW object stratified by A for the outcome model

summary

Summary of the average treatment effects from AIPW

AIPW

Augmented Inverse Probability Weighting

v0.6.3.2

GPL-3

Authors

Yongqi Zhong [aut, cre] (<https://orcid.org/0000-0002-4042-7450>), Ashley Naimi [aut] (<https://orcid.org/0000-0002-1510-8175>), Gabriel Conzuelo [ctb], Edward Kennedy [ctb]

Initial release

AIPW

Functions (12)

AIPW

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