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

Abstract

An increasing number of recent studies suggest doubly robust estimators with cross-fitting should be used when estimating causal effects with machine learning methods. However, existing programs that implement doubly robust estimators do not all support machine learning methods and cross-fitting, or provide estimates on multiplicative scales. To address these needs, we developed the AIPW package implementing the augmented inverse probability weighting (AIPW) estimation of average causal effects in R. Key features of the AIPW package includes cross-fitting and flexible covariate adjustment for observational studies and randomized trials (RCTs). In this paper, we use a simulated RCT to present the 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 shows that the xtbfAIPW package yielded comparable performance to other programs. Furthermore, we also found that cross-fitting substantively decreases the bias and improves the confidence interval coverage for doubly robust estimators fit 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.

Use of Machine Learning to Estimate the Per-Protocol Effect of Low-Dose Aspirin on Pregnancy Outcomes

“…21 To estimate this per-protocol effect of interest with machine learning methods, we used an AIPW estimator with an ensemble machine learner known as the Super Learner (or stacked generalization). 16,[22][23][24] Per-protocol effects were quantified on both the risk difference and the risk ratio scales for the pregnancy outcome.…”

“…Further, AIPW performs well, even when using flexible machine learning methods. 13,24 Using the aforementioned stacked machine learning algorithm, we estimated propensity scores by modeling the exposure with the aforementioned baseline covariates (exposure model) and constructed the outcome model using the exposure and those covariates. Cross-fitting, an additional layer of the fitting process on top of the stacking machine learning, is applied in the AIPW estimator to obtain valid inference (eg, low bias) and to further avoid overfitting.…”

“…Cross-fitting, an additional layer of the fitting process on top of the stacking machine learning, is applied in the AIPW estimator to obtain valid inference (eg, low bias) and to further avoid overfitting. 13,24 Sensitivity analyses were conducted by using other thresholds of time-fixed adherence status, which is a combination of adherence to at least 4, 5, and 6 days in a given week during at least 60%, 70%, and 80% of person-weeks of follow-up. In addition, we also provided the ITT estimates obtained via g-computation, 28 inverse probability weighting, 29 targeted maximum likelihood estimation, 30 and AIPW and unadjusted per-protocol effects (with different thresholds) estimated by g-computation, inverse probability weighting, targeted maximum likelihood estimation, and AIPW.…”

Demystifying statistical learning based on efficient influence functions

“…TMLE cross-validated estimators of counterfactual means and causal effects are also implemented in the R package drtmle. The R package AIPW (Yongqi Zhong et al, 2021) implements estimation of the ATE by AIPW (corresponding to the estimating equations or one-step estimator as we have seen) and also a TMLE estimator based on machine learning algorithms.…”

Use of Machine Learning Approaches and Statistical Techniques to Adjust for Nonadherence in Randomized Clinical Trials

“…They provided R code to implement this method in their Supplement 2 and have an associated AIPW R package that is publicly available. 4 This approach represents a compromise between intention-to-treat and per-protocol analyses using modern day statistical techniques and demonstrates how AIPW and machine learning techniques (eg, stacked learning) may be combined to properly adjust for nonadherence. I hope that this analysis will spark more collaborations between research clinicians and statisticians to tackle common problems in modern medical research, including nonrandomization, nonadherence, and other protocol deviations.Although not all statisticians may be aware of these methods, most PhD-level statisticians have the theoretical knowledge to implement the methods described in this study for other data sets.…”