ate: Estimating average effect of discrete treatment

Description Usage Arguments Value References Examples

View source: R/ate.R

Description

ate is used to estimate the mean outcome in a population had all subjects received given levels of a discrete (unconfounded) treatment.

Usage

1
2
ate(y, a, x, nsplits=2, sl.lib=c("SL.earth","SL.gam","SL.glm","SL.glmnet",
  "SL.glm.interaction", "SL.mean","SL.ranger","rpart"))

Arguments

y

outcome of interest.

a

discrete treatment.

x

covariate matrix.

nsplits

integer number of sample splits for nuisance estimation. If nsplits=1, sample splitting is not used, and nuisance functions are estimated on full sample (in which case validity of SEs/CIs requires empirical process conditions). Otherwise must have nsplits>1.

sl.lib

algorithm library for SuperLearner. Default library includes "earth", "gam", "glm", "glmnet", "glm.interaction", "mean", "ranger", "rpart.

Value

A list containing the following components:

res

estimates/SEs/CIs/p-values for population means and relevant contrasts.

nuis

subject-specific estimates of nuisance functions (i.e., propensity score and outcome regression)

ifvals

matrix of estimated influence function values.

References

Robins JM, Rotnitzky A (1995). Semiparametric efficiency in multivariate regression models with missing data. Journal of the American Statistical Association.

Hahn J (1998). On the role of the propensity score in efficient semiparametric estimation of average treatment effects. Econometrica.

van der Laan MJ, Robins JM (2003). Unified Methods for Censored Longitudinal Data and Causality (Springer).

Tsiatis AA (2006). Semiparametric Theory and Missing Data (Springer).

Robins JM, Li L, Tchetgen Tchetgen ET, van der Vaart A (2008). Higher order influence functions and minimax estimation of nonlinear functionals. Probability and Statistics: Essays in Honor of David A. Freedman.

Zheng W, van der Laan (2010). Asymptotic theory for cross-validated targeted maximum likelihood estimation UC Berkeley Division of Biostatistics Working Paper Series.

Chernozhukov V, Chetverikov V, Demirer M, et al (2016). Double machine learning for treatment and causal parameters.

Examples

1
2
3
4
n <- 1000; x <- matrix(rnorm(n*5),nrow=n)
a <- sample(3,n,replace=TRUE); y <- rnorm(n)

ate.res <- ate(y,a,x)

ehkennedy/npcausal documentation built on June 20, 2018, 4:24 a.m.