AIPW_tmle: Augmented Inverse Probability Weighting (AIPW) uses tmle or...

Description Details Value Constructor Public Methods Public Variables Examples

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 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

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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()

AIPW documentation built on June 11, 2021, 5:08 p.m.