Function to draw multiple potential outcomes, one for each condition that an assignment variable can be set to.
Arguments
- x
Formula describing the potential outcomes with the outcome name on the left hand side and the expression describing the potential outcomes on the right hand side, e.g.
Y ~ 0.1 * Z + rnorm(N)(this would draw two potential outcomes columns by default, named Y_Z_0 and Y_Z_1).- conditions
A list of conditions for each assignment variable. Defaults to
list(Z = c(0, 1)).- sep
Separator inserted between the outcome name and the assignment variable name used to construct the potential outcome variable names, defaults to "_".
Examples
fabricate(
N = 10,
U = rnorm(N),
potential_outcomes(Y ~ 0.1 * Z + U)
)
#> ID U Y_Z_0 Y_Z_1
#> 1 01 -0.330008991 -0.330008991 -0.23000899
#> 2 02 0.780017893 0.780017893 0.88001789
#> 3 03 0.246418030 0.246418030 0.34641803
#> 4 04 -0.171430937 -0.171430937 -0.07143094
#> 5 05 1.130348708 1.130348708 1.23034871
#> 6 06 0.470111747 0.470111747 0.57011175
#> 7 07 0.908487107 0.908487107 1.00848711
#> 8 08 -0.003946898 -0.003946898 0.09605310
#> 9 09 -0.626360877 -0.626360877 -0.52636088
#> 10 10 -0.695494396 -0.695494396 -0.59549440
# equivalently,
fabricate(
N = 10,
U = rnorm(N),
potential_outcomes(Y ~ 0.1 * Z + U,
conditions = list(Z = c(0, 1)))
)
#> ID U Y_Z_0 Y_Z_1
#> 1 01 1.72874460 1.72874460 1.8287446
#> 2 02 -0.34098278 -0.34098278 -0.2409828
#> 3 03 0.17407167 0.17407167 0.2740717
#> 4 04 -0.41202291 -0.41202291 -0.3120229
#> 5 05 0.08215034 0.08215034 0.1821503
#> 6 06 0.53497996 0.53497996 0.6349800
#> 7 07 0.03362362 0.03362362 0.1336236
#> 8 08 -0.67648623 -0.67648623 -0.5764862
#> 9 09 -0.48070807 -0.48070807 -0.3807081
#> 10 10 -0.65355094 -0.65355094 -0.5535509
fabricate(
N = 10,
U = rnorm(N),
potential_outcomes(Y ~ 0.1 * Z + U,
conditions = list(Z = c(1, 2, 3)))
)
#> ID U Y_Z_1 Y_Z_2 Y_Z_3
#> 1 01 -0.6837585 -0.5837585 -0.4837585 -0.3837585
#> 2 02 0.6372287 0.7372287 0.8372287 0.9372287
#> 3 03 -1.8956964 -1.7956964 -1.6956964 -1.5956964
#> 4 04 0.9875404 1.0875404 1.1875404 1.2875404
#> 5 05 1.3082691 1.4082691 1.5082691 1.6082691
#> 6 06 -0.4993503 -0.3993503 -0.2993503 -0.1993503
#> 7 07 0.7563782 0.8563782 0.9563782 1.0563782
#> 8 08 -0.5482245 -0.4482245 -0.3482245 -0.2482245
#> 9 09 -0.6847399 -0.5847399 -0.4847399 -0.3847399
#> 10 10 1.0310071 1.1310071 1.2310071 1.3310071
fabricate(
N = 10,
U = rnorm(N),
potential_outcomes(Y ~ 0.1 * Z1 + 0.3 * Z2 + 0.5 * Z1 * Z2 + U,
conditions = list(Z1 = c(0, 1),
Z2 = c(0, 1)))
)
#> ID U Y_Z1_0_Z2_0 Y_Z1_1_Z2_0 Y_Z1_0_Z2_1 Y_Z1_1_Z2_1
#> 1 01 -0.5810295 -0.5810295 -0.4810295 -0.2810295 0.3189705
#> 2 02 1.5482073 1.5482073 1.6482073 1.8482073 2.4482073
#> 3 03 -1.9621767 -1.9621767 -1.8621767 -1.6621767 -1.0621767
#> 4 04 0.4582724 0.4582724 0.5582724 0.7582724 1.3582724
#> 5 05 -1.1409825 -1.1409825 -1.0409825 -0.8409825 -0.2409825
#> 6 06 0.7079541 0.7079541 0.8079541 1.0079541 1.6079541
#> 7 07 1.4018953 1.4018953 1.5018953 1.7018953 2.3018953
#> 8 08 0.4797217 0.4797217 0.5797217 0.7797217 1.3797217
#> 9 09 -1.7046055 -1.7046055 -1.6046055 -1.4046055 -0.8046055
#> 10 10 0.6456383 0.6456383 0.7456383 0.9456383 1.5456383