# genouts: Generates hypothesized potential outcomes under a constant... In ri: ri: R package for performing randomization-based inference for experiments

## Description

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

## Usage

 `1` ```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`

## Author(s)

Peter M. Aronow <[email protected]>; Cyrus Samii <[email protected]>

## References

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

`estate`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```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 ```

### Example output

```\$two.tailed.p.value
[1] 0.1666667

\$two.tailed.p.value.abs
[1] 0.1944444

\$greater.p.value
[1] 0.08333333

\$lesser.p.value
[1] 0.9444444

\$quantile
2.5%     97.5%
-2.055556  2.222222

\$sd
[1] 1.440879

\$exp.val
[1] 1.048454e-16

\$two.tailed.p.value
[1] 1

\$two.tailed.p.value.abs
[1] 0.5

\$greater.p.value
[1] 0.5

\$lesser.p.value
[1] 0.5833333

\$quantile
2.5%     97.5%
0.2222222 3.6111111

\$sd
[1] 1.074393

\$exp.val
[1] 2
```

ri documentation built on May 2, 2019, 6:51 a.m.