ce_estimate_tmle_ate {CIMTx}

Causal inference with multiple treatments using TMLE for ATE effects

Description

The function ce_estimate_tmle_ate implements TMLE to estimate ATE effect with multiple treatments using observational data.

Usage

ce_estimate_tmle_ate(y, w, x, sl_library, ...)

Arguments

y

A numeric vector (0, 1) representing a binary outcome.

w

A numeric vector representing the treatment groups.

x

A dataframe, including all the covariates but not treatments.

sl_library

A character vector of prediction algorithms. A list of functions included in the SuperLearner package can be found with listWrappers.

...

Other parameters that can be passed through to functions.

Value

A summary of the effect estimates can be obtained with summary function.

References

Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.7. URL: https://CRAN.R-project.org/package=dplyr

Eric Polley, Erin LeDell, Chris Kennedy and Mark van der Laan (2021). SuperLearner: Super Learner Prediction. R package version 2.0-28. URL:https://CRAN.R-project.org/package=SuperLearner

Susan Gruber, Mark J. van der Laan (2012). tmle: An R Package for Targeted Maximum Likelihood Estimation. Journal of Statistical Software, 51(13), 1-35.