Package 'AIPW' reference manual

Title:	Augmented Inverse Probability Weighting
Description:	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.
Authors:	Yongqi Zhong [aut, cre] , Ashley Naimi [aut] , Gabriel Conzuelo [ctb], Edward Kennedy [ctb]
Maintainer:	Yongqi Zhong <[email protected]>
License:	GPL-3
Version:	0.6.9.1
Built:	2025-03-02 05:20:41 UTC
Source:	https://github.com/yqzhong7/aipw

Augmented Inverse Probability Weighting (AIPW)

Description

An R6Class of AIPW for estimating the average causal effects with users' inputs of exposure, outcome, covariates and related libraries for estimating the efficient influence function.

Details

An AIPW object is constructed by new() with users' inputs of data and causal structures, then it fit() the data using the libraries in Q.SL.library and g.SL.library with k_split cross-fitting, and provides results via the summary() method. After using fit() and/or summary() methods, propensity scores and inverse probability weights by exposure status can be examined with plot.p_score() and plot.ip_weights(), respectively.

If outcome is missing, analysis assumes missing at random (MAR) by estimating propensity scores of I(A=a, observed=1) with all covariates W. (W.Q and W.g are disabled.) Missing exposure is not supported.

See examples for illustration.

Value

AIPW object

Constructor

AIPW$new(Y = NULL, A = NULL, W = NULL, W.Q = NULL, W.g = NULL, Q.SL.library = NULL, g.SL.library = NULL, k_split = 10, verbose = TRUE, save.sl.fit = FALSE)

Constructor Arguments

Argument	Type	Details
`Y`	Integer	A vector of outcome (binary (0, 1) or continuous)
`A`	Integer	A vector of binary exposure (0 or 1)
`W`	Data	Covariates for both exposure and outcome models.
`W.Q`	Data	Covariates for the outcome model (Q).
`W.g`	Data	Covariates for the exposure model (g).
`Q.SL.library`	SL.library	Algorithms used for the outcome model (Q).
`g.SL.library`	SL.library	Algorithms used for the exposure model (g).
`k_split`	Integer	Number of folds for splitting (Default = 10).
`verbose`	Logical	Whether to print the result (Default = TRUE)
`save.sl.fit`	Logical	Whether to save Q.fit and g.fit (Default = FALSE)

Constructor Argument Details

W, W.Q & W.g: It can be a vector, matrix or data.frame. If and only if W == NULL, W would be replaced by W.Q and W.g.
Q.SL.library & g.SL.library: Machine learning algorithms from SuperLearner libraries or sl3 learner object (Lrnr_base)
k_split: It ranges from 1 to number of observation-1. If k_split=1, no cross-fitting; if k_split>=2, cross-fitting is used (e.g., k_split=10, use 9/10 of the data to estimate and the remaining 1/10 leftover to predict). NOTE: it's recommended to use cross-fitting.
save.sl.fit: This option allows users to save the fitted sl object (libs$Q.fit & libs$g.fit) for debug use. Warning: Saving the SuperLearner fitted object may cause a substantive storage/memory use.

Public Methods

Methods	Details	Link
`fit()`	Fit the data to the AIPW object	fit.AIPW
`stratified_fit()`	Fit the data to the AIPW object stratified by `A`	stratified_fit.AIPW
`summary()`	Summary of the average treatment effects from AIPW	summary.AIPW_base
`plot.p_score()`	Plot the propensity scores by exposure status	plot.p_score
`plot.ip_weights()`	Plot the inverse probability weights using truncated propensity scores	plot.ip_weights

Public Variables

Variable	Generated by	Return
`n`	Constructor	Number of observations
`stratified_fitted`	`stratified_fit()`	Fit the outcome model stratified by exposure status
`obs_est`	`fit()` & `summary()`	Components calculating average causal effects
`estimates`	`summary()`	A list of Risk difference, risk ratio, odds ratio
`result`	`summary()`	A matrix contains RD, ATT, ATC, RR and OR with their SE and 95%CI
`g.plot`	`plot.p_score()`	A density plot of propensity scores by exposure status
`ip_weights.plot`	`plot.ip_weights()`	A box plot of inverse probability weights
`libs`	`fit()`	SuperLearner or sl3 libraries and their fitted objects
`sl.fit`	Constructor	A wrapper function for fitting SuperLearner or sl3
`sl.predict`	Constructor	A wrapper function using `sl.fit` to predict

Public Variable Details

stratified_fit: An indicator for whether the outcome model is fitted stratified by exposure status in the fit() method. Only when using stratified_fit() to turn on stratified_fit = TRUE, summary outputs average treatment effects among the treated and the controls.
obs_est: After using fit() and summary() methods, this list contains the propensity scores (p_score), counterfactual predictions (mu, mu1 & mu0) and efficient influence functions (aipw_eif1 & aipw_eif0) for later average treatment effect calculations.
g.plot: This plot is generated by ggplot2::geom_density
ip_weights.plot: This plot uses truncated propensity scores stratified by exposure status (ggplot2::geom_boxplot)

References

Zhong Y, Kennedy EH, Bodnar LM, Naimi AI (2021). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology.

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.

Examples

library(SuperLearner)
library(ggplot2)

#create an object
aipw_sl <- AIPW$new(Y=rbinom(100,1,0.5), A=rbinom(100,1,0.5),
                    W.Q=rbinom(100,1,0.5), W.g=rbinom(100,1,0.5),
                    Q.SL.library="SL.mean",g.SL.library="SL.mean",
                    k_split=1,verbose=FALSE)

#fit the object
aipw_sl$fit()
# or use `aipw_sl$stratified_fit()` to estimate ATE and ATT/ATC

#calculate the results
aipw_sl$summary(g.bound = 0.025)

#check the propensity scores by exposure status after truncation
aipw_sl$plot.p_score()

library(SuperLearner)
library(ggplot2)

#create an object
aipw_sl <- AIPW$new(Y=rbinom(100,1,0.5), A=rbinom(100,1,0.5),
                    W.Q=rbinom(100,1,0.5), W.g=rbinom(100,1,0.5),
                    Q.SL.library="SL.mean",g.SL.library="SL.mean",
                    k_split=1,verbose=FALSE)

#fit the object
aipw_sl$fit()
# or use `aipw_sl$stratified_fit()` to estimate ATE and ATT/ATC

#calculate the results
aipw_sl$summary(g.bound = 0.025)

#check the propensity scores by exposure status after truncation
aipw_sl$plot.p_score()

Augmented Inverse Probability Weighting Base Class (AIPW_base)

Description

A base class for AIPW that implements the common methods, such as summary() and plot.p_score(), inheritted by AIPW and AIPW_tmle class

Format

R6Class object.

Value

AIPW base object

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

Description

AIPW_nuis class for users to manually input nuisance functions (estimates from the exposure and the outcome models)

Details

Create an AIPW_nuis object that uses users' input nuisance functions from the exposure model $P(A| W)$ , and the outcome models $P(Y| do(A=0), W)$ and $P(Y| do(A=1), W.Q)$ :

$\psi(a) = E{[ I(A=a) / P(A=a|W) ] * [Y-P(Y=1|A,W)] + P(Y=1| do(A=a),W) }$

Note: If outcome is missing, replace (A=a) with (A=a, observed=1) when estimating the propensity scores.

Value

AIPW_nuis object

Constructor

AIPW$new(Y = NULL, A = NULL, tmle_fit = NULL, verbose = TRUE)

Constructor Arguments

Argument	Type	Details
`Y`	Integer	A vector of outcome (binary (0, 1) or continuous)
`A`	Integer	A vector of binary exposure (0 or 1)
`mu0`	Numeric	User input of $P(Y=1\| do(A = 0),W_Q)$
`mu1`	Numeric	User input of $P(Y=1\| do(A = 1),W_Q)$
`raw_p_score`	Numeric	User input of $P(A=a\|W_g)$
`verbose`	Logical	Whether to print the result (Default = TRUE)
`stratified_fitted`	Logical	Whether mu0 & mu1 was estimated only using `A=0` & `A=1` (Default = FALSE)

Public Methods

Methods	Details	Link
`summary()`	Summary of the average treatment effects from AIPW	summary.AIPW_base
`plot.p_score()`	Plot the propensity scores by exposure status	plot.p_score
`plot.ip_weights()`	Plot the inverse probability weights using truncated propensity scores	plot.ip_weights

Public Variables

Variable	Generated by	Return
`n`	Constructor	Number of observations
`obs_est`	Constructor	Components calculating average causal effects
`estimates`	`summary()`	A list of Risk difference, risk ratio, odds ratio
`result`	`summary()`	A matrix contains RD, ATT, ATC, RR and OR with their SE and 95%CI
`g.plot`	`plot.p_score()`	A density plot of propensity scores by exposure status
`ip_weights.plot`	`plot.ip_weights()`	A box plot of inverse probability weights

Public Variable Details

stratified_fit: An indicator for whether the outcome model is fitted stratified by exposure status in thefit() method. Only when using stratified_fit() to turn on stratified_fit = TRUE, summary outputs average treatment effects among the treated and the controls.
obs_est: This list includes propensity scores (p_score), counterfactual predictions (mu, mu1 & mu0) and efficient influence functions (aipw_eif1 & aipw_eif0)
g.plot: This plot is generated by ggplot2::geom_density
ip_weights.plot: This plot uses truncated propensity scores stratified by exposure status (ggplot2::geom_boxplot)

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

Description

AIPW_tmle class uses a fitted tmle or tmle3 object as input

Details

Create an AIPW_tmle object that uses the estimated efficient influence function from a fitted tmle or tmle3 object

Value

AIPW_tmle object

Constructor

AIPW$new(Y = NULL, A = NULL, tmle_fit = NULL, verbose = TRUE)

Constructor Arguments

Argument	Type	Details
`Y`	Integer	A vector of outcome (binary (0, 1) or continuous)
`A`	Integer	A vector of binary exposure (0 or 1)
`tmle_fit`	Object	A fitted `tmle` or `tmle3` object
`verbose`	Logical	Whether to print the result (Default = TRUE)

Public Methods

Methods	Details	Link
`summary()`	Summary of the average treatment effects from AIPW	summary.AIPW_base
`plot.p_score()`	Plot the propensity scores by exposure status	plot.p_score
`plot.ip_weights()`	Plot the inverse probability weights using truncated propensity scores	plot.ip_weights

Public Variables

Variable	Generated by	Return
`n`	Constructor	Number of observations
`obs_est`	Constructor	Components calculating average causal effects
`estimates`	`summary()`	A list of Risk difference, risk ratio, odds ratio
`result`	`summary()`	A matrix contains RD, ATT, ATC, RR and OR with their SE and 95%CI
`g.plot`	`plot.p_score()`	A density plot of propensity scores by exposure status
`ip_weights.plot`	`plot.ip_weights()`	A box plot of inverse probability weights

Public Variable Details

obs_est: This list extracts from the fitted tmle or tmle3 object. It includes propensity scores (p_score), counterfactual predictions (mu, mu1 & mu0) and efficient influence functions (aipw_eif1 & aipw_eif0)
g.plot: This plot is generated by ggplot2::geom_density
ip_weights.plot: This plot uses truncated propensity scores stratified by exposure status (ggplot2::geom_boxplot)

Examples

## Not run: 
vec <- function() sample(0:1,100,replace = TRUE)
df <- data.frame(replicate(4,vec()))
names(df) <- c("A","Y","W1","W2")

## From tmle
library(tmle)
library(SuperLearner)
tmle_fit <- tmle(Y=df$Y,A=df$A,W=subset(df,select=c("W1","W2")),
                 Q.SL.library="SL.glm",
                 g.SL.library="SL.glm",
                 family="binomial")
AIPW_tmle$new(A=df$A,Y=df$Y,tmle_fit = tmle_fit,verbose = TRUE)$summary()


## From tmle3
# tmle3 simple implementation
library(tmle3)
library(sl3)
node_list <- list(A = "A",Y = "Y",W = c("W1","W2"))
or_spec <- tmle_OR(baseline_level = "0",contrast_level = "1")
tmle_task <- or_spec$make_tmle_task(df,node_list)
lrnr_glm <- make_learner(Lrnr_glm)
sl <- Lrnr_sl$new(learners = list(lrnr_glm))
learner_list <- list(A = sl, Y = sl)
tmle3_fit <- tmle3(or_spec, data=df, node_list, learner_list)

# parse tmle3_fit into AIPW_tmle class
AIPW_tmle$new(A=df$A,Y=df$Y,tmle_fit = tmle3_fit,verbose = TRUE)$summary()

## End(Not run)
## Not run: 
vec <- function() sample(0:1,100,replace = TRUE)
df <- data.frame(replicate(4,vec()))
names(df) <- c("A","Y","W1","W2")

## From tmle
library(tmle)
library(SuperLearner)
tmle_fit <- tmle(Y=df$Y,A=df$A,W=subset(df,select=c("W1","W2")),
                 Q.SL.library="SL.glm",
                 g.SL.library="SL.glm",
                 family="binomial")
AIPW_tmle$new(A=df$A,Y=df$Y,tmle_fit = tmle_fit,verbose = TRUE)$summary()


## From tmle3
# tmle3 simple implementation
library(tmle3)
library(sl3)
node_list <- list(A = "A",Y = "Y",W = c("W1","W2"))
or_spec <- tmle_OR(baseline_level = "0",contrast_level = "1")
tmle_task <- or_spec$make_tmle_task(df,node_list)
lrnr_glm <- make_learner(Lrnr_glm)
sl <- Lrnr_sl$new(learners = list(lrnr_glm))
learner_list <- list(A = sl, Y = sl)
tmle3_fit <- tmle3(or_spec, data=df, node_list, learner_list)

# parse tmle3_fit into AIPW_tmle class
AIPW_tmle$new(A=df$A,Y=df$Y,tmle_fit = tmle3_fit,verbose = TRUE)$summary()

## End(Not run)

AIPW wrapper function

Description

A wrapper function for AIPW$new()$fit()$summary()

Usage

aipw_wrapper(
  Y,
  A,
  verbose = TRUE,
  W = NULL,
  W.Q = NULL,
  W.g = NULL,
  Q.SL.library,
  g.SL.library,
  k_split = 10,
  g.bound = 0.025,
  stratified_fit = FALSE
)
aipw_wrapper(
  Y,
  A,
  verbose = TRUE,
  W = NULL,
  W.Q = NULL,
  W.g = NULL,
  Q.SL.library,
  g.SL.library,
  k_split = 10,
  g.bound = 0.025,
  stratified_fit = FALSE
)

Arguments

`Y`	Outcome (binary integer: 0 or 1)
`A`	Exposure (binary integer: 0 or 1)
`verbose`	Whether to print the result (logical; Default = FALSE)
`W`	covariates for both exposure and outcome models (vector, matrix or data.frame). If null, this function will seek for inputs from `W.Q` and `W.g`.
`W.Q`	Only valid when `W` is null, otherwise it would be replaced by `W`. Covariates for outcome model (vector, matrix or data.frame).
`W.g`	Only valid when `W` is null, otherwise it would be replaced by `W`. Covariates for exposure model (vector, matrix or data.frame)
`Q.SL.library`	SuperLearner libraries or sl3 learner object (Lrnr_base) for outcome model
`g.SL.library`	SuperLearner libraries or sl3 learner object (Lrnr_base) for exposure model
`k_split`	Number of splitting (integer; range: from 1 to number of observation-1): if k_split=1, no cross-fitting; if k_split>=2, cross-fitting is used (e.g., `k_split=10`, use 9/10 of the data to estimate and the remaining 1/10 leftover to predict). NOTE: it's recommended to use cross-fitting.
`g.bound`	Value between [0,1] at which the propensity score should be truncated. Defaults to 0.025.
`stratified_fit`	An indicator for whether the outcome model is fitted stratified by exposure status in the`fit()` method. Only when using `stratified_fit()` to turn on `stratified_fit = TRUE`, `summary` outputs average treatment effects among the treated and the controls.

Value

A fitted AIPW object with summarised results

Examples

library(SuperLearner)
aipw_sl <- aipw_wrapper(Y=rbinom(100,1,0.5), A=rbinom(100,1,0.5),
                    W.Q=rbinom(100,1,0.5), W.g=rbinom(100,1,0.5),
                    Q.SL.library="SL.mean",g.SL.library="SL.mean",
                    k_split=1,verbose=FALSE)
library(SuperLearner)
aipw_sl <- aipw_wrapper(Y=rbinom(100,1,0.5), A=rbinom(100,1,0.5),
                    W.Q=rbinom(100,1,0.5), W.g=rbinom(100,1,0.5),
                    Q.SL.library="SL.mean",g.SL.library="SL.mean",
                    k_split=1,verbose=FALSE)

Simulated Observational Study

Description

Datasets were simulated using baseline covariates (sampling with replacement) from the Effects of Aspirin in Gestation and Reproduction (EAGeR) study. Data generating mechanisms were described in our manuscript (Zhong et al. (inpreparation), Am. J. Epidemiol.). True marginal causal effects on risk difference, log risk ratio and log odds ratio scales were attached to the dataset attributes (true_rd, true_logrr,true_logor).

Usage

data(eager_sim_obs)
data(eager_sim_obs)

Format

An object of class data.frame with 200 rows and 8 columns:

sim_Y: binary, simulated outcome which is condition on all other covariates in the dataset
sim_A: binary, simulated exposure which is conditon on all other covarites expect sim_Y.
eligibility: binary, indicator of the eligibility stratum
loss_num: count, number of prior pregnancy losses
age: continuous, age in years
time_try_pregnant: count, months of conception attempts prior to randomization
BMI: continuous, body mass index
meanAP: continuous, mean arterial blood pressure

References

Schisterman, E.F., Silver, R.M., Lesher, L.L., Faraggi, D., Wactawski-Wende, J., Townsend, J.M., Lynch, A.M., Perkins, N.J., Mumford, S.L. and Galai, N., 2014. Preconception low-dose aspirin and pregnancy outcomes: results from the EAGeR randomised trial. The Lancet, 384(9937), pp.29-36.

Zhong, Y., Naimi, A.I., Kennedy, E.H., (In preparation). AIPW: An R package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology

Simulated Randomized Trial

Description

Datasets were simulated using baseline covariates (sampling with replacement) from the Effects of Aspirin in Gestation and Reproduction (EAGeR) study.

Usage

data(eager_sim_rct)
data(eager_sim_rct)

Format

An object of class data.frame with 1228 rows and 8 columns:

sim_Y: binary, simulated outcome which is condition on all other covariates in the dataset
sim_T: binary, simulated treatment which is condition on eligibility only.
eligibility: binary, indicator of the eligibility stratum
loss_num: count, number of prior pregnancy losses
age: continuous, age in years
time_try_pregnant: count, months of conception attempts prior to randomization
BMI: continuous, body mass index
meanAP: continuous, mean arterial blood pressure

References

Zhong, Y., Naimi, A.I., Kennedy, E.H., (In preparation). AIPW: An R package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology

Fit the data to the AIPW object

Description

Fitting the data into the AIPW object with/without cross-fitting to estimate the efficient influence functions

Value

A fitted AIPW object with obs_est and libs (public variables)

R6 Usage

$fit()

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

Description

Plot and check the balance of propensity scores by exposure status

Value

ip_weights.plot (public variable): A box plot of inverse probability weights using truncated propensity scores by exposure status (ggplot2::geom_boxplot)

R6 Usage

$plot.ip_weights()

Plot the propensity scores by exposure status

Description

Plot and check the balance of propensity scores by exposure status

Value

g.plot (public variable): A density plot of propensity scores by exposure status (ggplot2::geom_density)

R6 Usage

$plot.p_plot()

Repeated Crossfitting Procedure for AIPW

Description

An R6Class that allows repeated crossfitting procedure for an AIPW object

Details

See examples for illustration.

Value

AIPW object

Constructor

Repeated$new(aipw_obj = NULL)

Constructor Arguments

Argument	Type	Details
`aipw_obj`	AIPW object	an AIPW object

Public Methods

Methods	Details	Link
`repfit()`	Fit the data to the AIPW object `num_reps` times	repfit.Repeated
`summary_median()`	Summary (median) of estimates from the `repfit()`	summary_median.Repeated

Public Variables

Variable	Generated by	Return
`repeated_estimates`	`repfit()`	A data.frame of estiamtes form `num_reps` cross-fitting
`repeated_results`	`summary_median()`	A list of sumarised estimates
`result`	`summary_median()`	A data.frame of sumarised estimates

Public Variable Details

repeated_estimates: Estimates from num_reps cross-fitting.
result: Summarised estimates from “repeated_estimates' using median methods.

References

Zhong Y, Kennedy EH, Bodnar LM, Naimi AI (2021). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology.

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.

Examples

library(SuperLearner)
library(ggplot2)

#create an object
aipw_sl <- AIPW$new(Y=rbinom(100,1,0.5), A=rbinom(100,1,0.5),
                    W.Q=rbinom(100,1,0.5), W.g=rbinom(100,1,0.5),
                    Q.SL.library="SL.mean",g.SL.library="SL.mean",
                    k_split=2,verbose=FALSE)

#create a repeated crossfitting object from the previous step
repeated_aipw_sl <- Repeated$new(aipw_sl)

#fit repetitively (stratified = TRUE will use stratified_fit() method in AIPW class)
repeated_aipw_sl$repfit(num_reps = 3, stratified = FALSE)

#summarise the results
repeated_aipw_sl$summary_median()

library(SuperLearner)
library(ggplot2)

#create an object
aipw_sl <- AIPW$new(Y=rbinom(100,1,0.5), A=rbinom(100,1,0.5),
                    W.Q=rbinom(100,1,0.5), W.g=rbinom(100,1,0.5),
                    Q.SL.library="SL.mean",g.SL.library="SL.mean",
                    k_split=2,verbose=FALSE)

#create a repeated crossfitting object from the previous step
repeated_aipw_sl <- Repeated$new(aipw_sl)

#fit repetitively (stratified = TRUE will use stratified_fit() method in AIPW class)
repeated_aipw_sl$repfit(num_reps = 3, stratified = FALSE)

#summarise the results
repeated_aipw_sl$summary_median()

Fit the data to the AIPW object repeatedly

Description

Fitting the data into the AIPW object with cross-fitting repeatedly to obtain multiple estimates from repetitions to avoid randomness due to splits in cross-fitting

Arguments

`num_reps`	Integer. Number of repetition of cross-fitting procedures (`fit()` or `stratified_fit()` see blow).
`stratified`	Boolean. `stratified = TRUE` will use `stratified_fit()` in the AIPW object to cross-fitting.

Value

A Repeated object with repeated_estimates (estimates from num_reps times repetition)

R6 Usage

$repfit(num_reps = 20, stratified = FALSE)

References

Chernozhukov V, Chetverikov V, Demirer M, et al (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal.

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

Description

Fitting the data into the AIPW object with/without cross-fitting to estimate the efficient influence functions. Outcome model is fitted, stratified by exposure status A

Value

A fitted AIPW object with obs_est and libs (public variables)

R6 Usage

$stratified_fit.AIPW()

Summary of the average treatment effects from AIPW

Description

Calculate average causal effects in RD, RR and OR in the fitted AIPW or AIPW_tmle object using the estimated efficient influence functions

Arguments

g.bound

Value between [0,1] at which the propensity score should be truncated. Propensity score will be truncated to $[g.bound, 1-g.bound]$ when one g.bound value is provided, or to $[min(g.bound), max(g.bound)]$ when two values are provided. Defaults to 0.025.

Value

estimates and result (public variables): Risks, Average treatment effect in RD, RR and OR.

R6 Usage

$summary(g.bound = 0.025)
$summary(g.bound = c(0.025,0.975))

Summary of the `repeated_estimates` from `repfit()` in the Repeated object using median methods.

Description

From repeated_estimates, calculate the median estimate (median(Estimates)), median SE (median(SE)), SE adjusting for variations across num_reps times, and 95% CI using SE adjusting for SE adjusted for variability.

Value

repeated_results and result (public variables).

R6 Usage

$summary_median.Repeated()

References

Chernozhukov V, Chetverikov V, Demirer M, et al (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal.

Package 'AIPW'

Help Index

Augmented Inverse Probability Weighting (AIPW)

Description

Details

Value

Constructor

Constructor Arguments

Constructor Argument Details

Public Methods

Public Variables

Public Variable Details

References

Examples

Augmented Inverse Probability Weighting Base Class (AIPW_base)

Description

Format

Value

See Also

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

Description

Details

Value

Constructor

Constructor Arguments

Public Methods

Public Variables

Public Variable Details

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

Description

Details

Value

Constructor

Constructor Arguments

Public Methods

Public Variables

Public Variable Details

Examples

AIPW wrapper function

Description

Usage

Arguments

Value

See Also

Examples

Simulated Observational Study

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Usage

Format

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See Also

Simulated Randomized Trial

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Usage

Format

References

See Also

Fit the data to the AIPW object

Description

Value

R6 Usage

See Also

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

Description

Value

R6 Usage

See Also

Plot the propensity scores by exposure status

Description

Value

R6 Usage

See Also

Repeated Crossfitting Procedure for AIPW

Description

Details

Value

Constructor

Constructor Arguments

Public Methods

Public Variables

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

Summary of the `repeated_estimates` from `repfit()` in the Repeated object using median methods.