att: Estimating average effect of treatment on the treated

In ehkennedy/npcausal: Nonparametric causal inference methods

Description

att is used to estimate the difference in mean outcome among treated subjects had a binary (unconfounded) treatment been withheld.

Usage

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

Arguments

y	outcome of interest.
a	binary 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 if using SuperLearner. Default library includes "earth", "gam", "glm", "glmnet", "glm.interaction", "mean", and "ranger".

Value

A list containing the following components:

res	estimates/SEs/CIs/p-values for treated means and contrast.
nuis	subject-specific estimates of nuisance functions (i.e., propensity score and outcome regression)
ifvals	vector of estimated influence function values.

References

(Also see references for function ate)

Kennedy EH, Sjolander A, Small DS (2015). Semiparametric causal inference in matched cohort studies. Biometrika.

Examples

n <- 100; x <- matrix(rnorm(n*5),nrow=n)
a <- rbinom(n,1,.3); y <- rnorm(n)

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

ehkennedy/npcausal documentation built on Feb. 26, 2021, 2:43 a.m.