Repeated: Repeated Crossfitting Procedure for AIPW

In AIPW: Augmented Inverse Probability Weighting

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()

AIPW documentation built on April 12, 2025, 1:27 a.m.