Augmented Inverse Probability Weighting (AIPW) uses tmle or tmle3 as inputs
AIPW_tmle class uses a fitted tmle or tmle3 object as input
Create an AIPW_tmle object that uses the estimated efficient influence function from a fitted tmle or tmle3 object
AIPW_tmle object
AIPW$new(Y = NULL, A = NULL, tmle_fit = NULL, verbose = TRUE)
| Argument | Type | Details |
Y |
Integer | A vector of outcome (binary (0, 1) or continuous) |
A |
Integer | A vector of binary exposure (0 or 1) |
tmle_fit |
Object | A fitted tmle or tmle3 object |
verbose |
Logical | Whether to print the result (Default = TRUE) |
| Methods | Details | Link |
summary() |
Summary of the average treatment effects from AIPW | summary.AIPW_base |
plot.p_score() |
Plot the propensity scores by exposure status | plot.p_score |
plot.ip_weights() |
Plot the inverse probability weights using truncated propensity scores | plot.ip_weights |
| Variable | Generated by | Return |
n |
Constructor | Number of observations |
obs_est |
Constructor | Components calculating average causal effects |
estimates |
summary() |
A list of Risk difference, risk ratio, odds ratio |
result |
summary() |
A matrix contains RD, ATT, ATC, RR and OR with their SE and 95%CI |
g.plot |
plot.p_score() |
A density plot of propensity scores by exposure status |
ip_weights.plot |
plot.ip_weights() |
A box plot of inverse probability weights |
obs_estThis list extracts from the fitted tmle object.
It includes propensity scores (p_score), counterfactual predictions (mu, mu1 & mu0) and efficient influence functions (aipw_eif1 & aipw_eif0)
g.plotThis plot is generated by ggplot2::geom_density
ip_weights.plotThis plot uses truncated propensity scores stratified by exposure status (ggplot2::geom_boxplot)
vec <- function() sample(0:1,100,replace = TRUE)
df <- data.frame(replicate(4,vec()))
names(df) <- c("A","Y","W1","W2")
## From tmle
library(tmle)
library(SuperLearner)
tmle_fit <- tmle(Y=df$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()Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.