estate: Estimation of average treatment effects
In ri: ri: R package for performing randomization-based inference for experiments

Description Usage Arguments Value Author(s) References See Also Examples

Function for estimating the average treatment effect (ATE). Permits regression adjustment for covariates, difference estimation (with a pretreatment measure of the outcome variable), inverse probability weighting, and unbiased Horvitz-Thompson estimation.

1	estate(Y, Z, X = NULL, Ypre = NULL, prob = NULL, HT = FALSE)

`Y`	numeric vector of length N, outcome variable
`Z`	binary vector (0 or 1) of length N, treatment indicator
`X`	N-by-k numeric matrix of covariates for regression adjustment
`Ypre`	numeric vector of length N, pretreatment measure of the outcome variable for difference estimation
`prob`	numeric vector within the (0,1) interval of length N, probability of treatment assignment, as outputted by `genprob()` or `genprobexact()`. When `prob=NULL` (the default), assumes uniform probability of assignment to treatment equal to the mean of Z
`HT`	when `HT=TRUE`, invokes the Horvitz-Thompson (difference-in-totals) estimator. When `HT=FALSE`, invokes the inverse-probability-weighted regression estimator

a scalar, the estimated average treatment effect

Peter M. Aronow <peter.aronow@yale.edu>; Cyrus Samii <cds2083@nyu.edu>

Peter M. Aronow and Joel A. Middleton. 2012. A Class of Unbiased Estimators of the Average Treatment Effect in Randomized Experiments. Working paper, Yale University. http://pantheon.yale.edu/~pma5/unbiasedestimators.pdf

Gerber, Alan S. and Donald P. Green. 2012. Field Experiments: Design, Analysis, and Interpretation. New York: W.W. Norton.

Horvitz, D.G. and D.J. Thompson. 1952. A generalization of sampling without replacement from a finite universe. J. Amer. Statist. Assoc. 47 663-684.

genprob

y <- c(8,6,2,0,3,1,1,1,2,2,0,1,0,2,2,4,1,1) 
Z <- c(1,1,0,0,1,1,0,0,1,1,1,1,0,0,1,1,0,0)
cluster <- c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9)
block <- c(rep(1,4),rep(2,6),rep(3,8))

probs <- genprobexact(Z,blockvar=block, clustvar=cluster) # probability of treatment
ate <- estate(y,Z,prob=probs) # estimate the ATE