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

Description

AIPW_tmle class uses a fitted tmle or tmle3 object as input

Arguments

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)

Details

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

Examples

Run this code

if (FALSE) {
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()
}

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