Contributors: Yongqi Zhong, Ashley Naimi, Gabriel Conzuelo, Edward Kennedy
Augmented inverse probability weighting (AIPW) is a doubly robust
estimator for causal inference. The AIPW package is designed for
estimating the average treatment effect of a binary exposure on risk
difference (RD), risk ratio (RR) and odds ratio (OR) scales with
user-defined stacked machine learning algorithms
(SuperLearner or
sl3). Users need to examine causal
assumptions (e.g., consistency) before using this package.
If you find this package is helpful, please consider to cite:
@article{zhong_aipw_2021,
author = {Zhong, Yongqi and Kennedy, Edward H and Bodnar, Lisa M and Naimi, Ashley I},
title = {AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects},
journal = {American Journal of Epidemiology},
year = {2021},
month = {07},
issn = {0002-9262},
doi = {10.1093/aje/kwab207},
url = {https://doi.org/10.1093/aje/kwab207},
}
The major new feature introduced is the Repeated class, which allows
for repeated cross-fitting procedures to mitigate randomness due to data
splits in machine learning-based estimation as suggested by Chernozhukov
et al. (2018). This feature: - Enables running the cross-fitting
procedure multiple times to produce more stable estimates - Provides
methods to summarize results using median-based approaches - Supports
parallelization with future.apply - Includes visualization of estimate
distributions across repetitions - See the Repeated Cross-fitting
vignette for more details
- Fixed handling of continuous outcomes for exposure models (#50)
- Improved handling of non-binary treatments
- Fixed Q.model for continuous outcomes
- Updated GitHub Actions workflows for R-CMD-check, test coverage, and pkgdown
- Removed Travis CI in favor of GitHub Actions
- Enhanced test coverage with additional tests for the new Repeated class
- Updated documentation and namespace for new functionality
- New GitHub versions (after v0.6.3.1) no longer support sl3 and tmle3
- Users requiring sl3 and tmle3 support should install via
remotes::install_github("yqzhong7/AIPW@aje_version")
- Fixed repeated fitting when stratified_fit is enabled
- Fixed handling of Q.model
- Added proper error handling for various edge cases
- Fixed continuous outcome for exposure model
- Improved cross-fitting to reduce randomness (#38)
install.packages("AIPW")install.packages("remotes")
remotes::install_github("yqzhong7/AIPW")* CRAN version only supports SuperLearner and tmle. New GitHub
versions (after v0.6.3.1) no longer support sl3 and tmle3. If you are
still interested in using the version with sl3 and tmle3 support, please
install remotes::install_github("yqzhong7/AIPW@aje_version") Please
install the Github version (master branch) if you choose to use sl3 and
tmle3.
set.seed(888)
data("eager_sim_obs")
outcome <- eager_sim_obs$sim_Y
exposure <- eager_sim_obs$sim_A
#covariates for both outcome model (Q) and exposure model (g)
covariates <- as.matrix(eager_sim_obs[-1:-2])
# covariates <- c(rbinom(N,1,0.4)) #a vector of a single covariate is also supportedlibrary(AIPW)
library(SuperLearner)
#> Loading required package: nnls
#> Loading required package: gam
#> Loading required package: splines
#> Loading required package: foreach
#> Loaded gam 1.20.2
#> Super Learner
#> Version: 2.0-28
#> Package created on 2021-05-04
library(ggplot2)
AIPW_SL <- AIPW$new(Y = outcome,
A = exposure,
W = covariates,
Q.SL.library = c("SL.mean","SL.glm"),
g.SL.library = c("SL.mean","SL.glm"),
k_split = 3,
verbose=FALSE)$
fit()$
#Default truncation
summary(g.bound = 0.025)$
plot.p_score()$
plot.ip_weights()
To see the results, set verbose = TRUE(default) or:
print(AIPW_SL$result, digits = 2)
#> Estimate SE 95% LCL 95% UCL N
#> Risk of Exposure 0.44 0.046 0.3528 0.53 118
#> Risk of Control 0.31 0.051 0.2061 0.41 82
#> Risk Difference 0.14 0.068 0.0048 0.27 200
#> Risk Ratio 1.45 0.191 0.9974 2.11 200
#> Odds Ratio 1.81 0.295 1.0144 3.22 200To obtain average treatment effect among the treated/controls (ATT/ATC),
statified_fit() must be used:
AIPW_SL_att <- AIPW$new(Y = outcome,
A = exposure,
W = covariates,
Q.SL.library = c("SL.mean","SL.glm"),
g.SL.library = c("SL.mean","SL.glm"),
k_split = 3,
verbose=T)
suppressWarnings({
AIPW_SL_att$stratified_fit()$summary()
})
#> Done!
#> Estimate SE 95% LCL 95% UCL N
#> Risk of Exposure 0.4352 0.0467 0.34362 0.527 118
#> Risk of Control 0.3244 0.0513 0.22385 0.425 82
#> Risk Difference 0.1108 0.0684 -0.02320 0.245 200
#> Risk Ratio 1.3416 0.1858 0.93210 1.931 200
#> Odds Ratio 1.6048 0.2927 0.90429 2.848 200
#> ATT Risk Difference 0.0991 0.0880 -0.07339 0.272 200
#> ATC Risk Difference 0.1148 0.0634 -0.00946 0.239 200You can also use the aipw_wrapper() to wrap new(), fit() and
summary() together (also support method chaining):
AIPW_SL <- aipw_wrapper(Y = outcome,
A = exposure,
W = covariates,
Q.SL.library = c("SL.mean","SL.glm"),
g.SL.library = c("SL.mean","SL.glm"),
k_split = 3,
verbose=TRUE,
stratified_fit=F)$plot.p_score()$plot.ip_weights()The Repeated class allows for repeated cross-fitting procedures to
mitigate randomness due to data splits. This approach is recommended in
machine learning-based estimation as suggested by Chernozhukov et
al. (2018).
library(SuperLearner)
library(ggplot2)
# First create a regular AIPW object
aipw_obj <- AIPW$new(Y = outcome,
A = exposure,
W = covariates,
Q.SL.library = c("SL.mean","SL.glm"),
g.SL.library = c("SL.mean","SL.glm"),
k_split = 3,
verbose = FALSE)
# Create a repeated fitting object from the AIPW object
repeated_aipw <- Repeated$new(aipw_obj)
# Perform repeated fitting 20 times
repeated_aipw$repfit(num_reps = 20, stratified = FALSE)
# Summarize results using median-based methods
repeated_aipw$summary_median()
# You can also visualize the distribution of estimates across repetitions
estimates_df <- repeated_aipw$repeated_estimates
ggplot(estimates_df, aes(x = Estimate, fill = Estimand)) +
geom_density(alpha = 0.5) +
theme_minimal() +
labs(title = "Distribution of Estimates Across Repeated Fittings",
subtitle = "Based on 20 repetitions",
x = "Estimate Value",
y = "Density")Setting stratified = TRUE in the repfit() function will use the
stratified fitting procedure for each repetition:
# Using stratified fitting
repeated_aipw_strat <- Repeated$new(aipw_obj)
repeated_aipw_strat$repfit(num_reps = 20, stratified = TRUE)
repeated_aipw_strat$summary_median()Note that the Repeated class also supports parallelization with
future.apply as described below.
In default setting, the AIPW$fit() method will be run sequentially.
The current version of AIPW package supports parallel processing
implemented by
future.apply package
under the future framework.
Simply use future::plan() to enable parallelization and set.seed()
to take care of the random number generation (RNG) problem:
###Additional steps for parallel processing###
# install.packages("future.apply")
library(future.apply)
#> Loading required package: future
future::plan(multiprocess, workers=2, gc=T)
#> Warning: Strategy 'multiprocess' is deprecated in future (>= 1.20.0)
#> [2020-10-30]. Instead, explicitly specify either 'multisession' (recommended) or
#> 'multicore'. In the current R session, 'multiprocess' equals 'multisession'.
#> Warning in supportsMulticoreAndRStudio(...): [ONE-TIME WARNING] Forked
#> processing ('multicore') is not supported when running R from RStudio
#> because it is considered unstable. For more details, how to control forked
#> processing or not, and how to silence this warning in future R sessions, see ?
#> parallelly::supportsMulticore
set.seed(888)
###Same procedure for AIPW as described above###
AIPW_SL <- AIPW$new(Y = outcome,
A = exposure,
W = covariates,
Q.SL.library = c("SL.mean","SL.glm"),
g.SL.library = c("SL.mean","SL.glm"),
k_split = 3,
verbose=TRUE)$fit()$summary()
#> Done!
#> Estimate SE 95% LCL 95% UCL N
#> Risk of Exposure 0.443 0.0462 0.35284 0.534 118
#> Risk of Control 0.306 0.0510 0.20607 0.406 82
#> Risk Difference 0.137 0.0677 0.00482 0.270 200
#> Risk Ratio 1.449 0.1906 0.99741 2.106 200
#> Odds Ratio 1.807 0.2946 1.01442 3.219 200Progress bar that supports parallel processing is available in the
AIPW$fit() method through the API from
progressr package:
library(progressr)
#define the type of progress bar
handlers("progress")
#reporting through progressr::with_progress() which is embedded in the AIPW$fit() method
with_progress({
AIPW_SL <- AIPW$new(Y = outcome,
A = exposure,
W = covariates,
Q.SL.library = c("SL.mean","SL.glm"),
g.SL.library = c("SL.mean","SL.glm"),
k_split = 3,
verbose=FALSE)$fit()$summary()
})
#also available for the wrapper
with_progress({
AIPW_SL <- aipw_wrapper(Y = outcome,
A = exposure,
W = covariates,
Q.SL.library = c("SL.mean","SL.glm"),
g.SL.library = c("SL.mean","SL.glm"),
k_split = 3,
verbose=FALSE)
})AIPW_tmle class is designed for using tmle/tmle3 fitted object as
input
require(tmle)
#> Loading required package: tmle
#> Loading required package: glmnet
#> Loading required package: Matrix
#> Loaded glmnet 4.1-6
#> Welcome to the tmle package, version 1.5.0-1.1
#>
#> Major changes since v1.3.x. Use tmleNews() to see details on changes and bug fixes
require(SuperLearner)
tmle_fit <- tmle(Y = as.vector(outcome), A = as.vector(exposure),W = covariates,
Q.SL.library=c("SL.mean","SL.glm"),
g.SL.library=c("SL.mean","SL.glm"),
family="binomial")
tmle_fit
#> Additive Effect
#> Parameter Estimate: 0.12795
#> Estimated Variance: 0.0043047
#> p-value: 0.051161
#> 95% Conf Interval: (-0.00064797, 0.25654)
#>
#> Additive Effect among the Treated
#> Parameter Estimate: 0.13118
#> Estimated Variance: 0.0045329
#> p-value: 0.051365
#> 95% Conf Interval: (-0.00077957, 0.26314)
#>
#> Additive Effect among the Controls
#> Parameter Estimate: 0.12446
#> Estimated Variance: 0.00414
#> p-value: 0.05307
#> 95% Conf Interval: (-0.0016502, 0.25057)
#>
#> Relative Risk
#> Parameter Estimate: 1.4093
#> p-value: 0.064957
#> 95% Conf Interval: (0.97895, 2.0288)
#>
#> log(RR): 0.34308
#> variance(log(RR)): 0.034558
#>
#> Odds Ratio
#> Parameter Estimate: 1.7316
#> p-value: 0.057367
#> 95% Conf Interval: (0.98296, 3.0504)
#>
#> log(OR): 0.54905
#> variance(log(OR)): 0.083461
#extract fitted tmle object to AIPW
AIPW_tmle$
new(A=exposure,Y=outcome,tmle_fit = tmle_fit,verbose = TRUE)$
summary(g.bound=0.025)
#> Cross-fitting is supported only within the outcome model from a fitted tmle object (with cvQinit = TRUE)
#> Estimate SE 95% LCL 95% UCL N
#> Risk of Exposure 0.441 0.0447 0.352877 0.528 118
#> Risk of Control 0.313 0.0503 0.214003 0.411 82
#> Risk Difference 0.128 0.0656 -0.000648 0.257 200
#> Risk Ratio 1.409 0.1814 0.987632 2.011 200
#> Odds Ratio 1.732 0.2814 0.997604 3.006 200__New GitHub versions (after v0.6.3.1) no longer support sl3 and tmle3. If you are still interested in using the version with sl3 and tmle3 support, please install `remotes::install_github(“yqzhong7/AIPW@aje_version”)__
remotes::install_github("yqzhong7/AIPW@aje_version")
library(sl3)
library(tmle3)
node_list <- list(A = "sim_A",Y = "sim_Y",W = colnames(eager_sim_obs)[-1:-2])
or_spec <- tmle_OR(baseline_level = "0",contrast_level = "1")
tmle_task <- or_spec$make_tmle_task(eager_sim_obs,node_list)
lrnr_glm <- make_learner(Lrnr_glm)
lrnr_mean <- make_learner(Lrnr_mean)
sl <- Lrnr_sl$new(learners = list(lrnr_glm,lrnr_mean))
learner_list <- list(A = sl, Y = sl)
tmle3_fit <- tmle3(or_spec, data=eager_sim_obs, node_list, learner_list)
# parse tmle3_fit into AIPW_tmle class
AIPW_tmle$
new(A=eager_sim_obs$sim_A,Y=eager_sim_obs$sim_Y,tmle_fit = tmle3_fit,verbose = TRUE)$
summary()Robins JM, Rotnitzky A (1995). Semiparametric efficiency in multivariate regression models with missing data. Journal of the American Statistical Association.
Chernozhukov V, Chetverikov V, Demirer M, et al (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal.
Kennedy EH, Sjolander A, Small DS (2015). Semiparametric causal inference in matched cohort studies. Biometrika.
Pearl, J., 2009. Causality. Cambridge university press.