ri (version 0.9)

genouts: Generates hypothesized potential outcomes under a constant effects hypothesis

Description

Takes an outcome variable, a treatment assignment, and a hypothesized treatment effect and generates a set of hypothesized potential outcomes

Usage

genouts(Y, Z, ate = 0)

Arguments

Y

numeric vector of N-length, outcome variable

Z

binary vector (0 or 1) of N-length, treatment indicator

ate

numeric scalar, hypothesized treatment effect

Value

list consisting of two N-length numeric vectors labeled Y0 and Y1

References

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

See Also

estate

Examples

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))

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

## Conduct Sharp Null Hypothesis Test of Zero Effect for Each Unit

Ys <- genouts(y,Z,ate=0) # generate potential outcomes under sharp null of no effect
distout <- gendist(Ys,perms, prob=probs) # generate sampling dist. under sharp null
dispdist(distout, ate)  # display characteristics of sampling dist. for inference

## Generate Sampling Distribution Around Estimated ATE

Ys <- genouts(y,Z,ate=ate) ## generate potential outcomes under tau = ATE
distout <- gendist(Ys,perms, prob=probs) # generate sampling dist. under tau = ATE
dispdist(distout, ate)  ## display characteristics of sampling dist. for inference