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 0.17407167 0.17407167 0.2740717
#> 2 02 -0.41202291 -0.41202291 -0.3120229
#> 3 03 0.08215034 0.08215034 0.1821503
#> 4 04 0.53497996 0.53497996 0.6349800
#> 5 05 0.03362362 0.03362362 0.1336236
#> 6 06 -0.67648623 -0.67648623 -0.5764862
#> 7 07 -0.48070807 -0.48070807 -0.3807081
#> 8 08 -0.65355094 -0.65355094 -0.5535509
#> 9 09 -0.68375847 -0.68375847 -0.5837585
#> 10 10 0.63722867 0.63722867 0.7372287
# 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 -1.8956964 -1.8956964 -1.7956964
#> 2 02 0.9875404 0.9875404 1.0875404
#> 3 03 1.3082691 1.3082691 1.4082691
#> 4 04 -0.4993503 -0.4993503 -0.3993503
#> 5 05 0.7563782 0.7563782 0.8563782
#> 6 06 -0.5482245 -0.5482245 -0.4482245
#> 7 07 -0.6847399 -0.6847399 -0.5847399
#> 8 08 1.0310071 1.0310071 1.1310071
#> 9 09 -0.5810295 -0.5810295 -0.4810295
#> 10 10 1.5482073 1.5482073 1.6482073
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 -1.96217674 -1.86217674 -1.7621767 -1.6621767
#> 2 02 0.45827243 0.55827243 0.6582724 0.7582724
#> 3 03 -1.14098248 -1.04098248 -0.9409825 -0.8409825
#> 4 04 0.70795415 0.80795415 0.9079541 1.0079541
#> 5 05 1.40189529 1.50189529 1.6018953 1.7018953
#> 6 06 0.47972165 0.57972165 0.6797217 0.7797217
#> 7 07 -1.70460548 -1.60460548 -1.5046055 -1.4046055
#> 8 08 0.64563834 0.74563834 0.8456383 0.9456383
#> 9 09 0.54523742 0.64523742 0.7452374 0.8452374
#> 10 10 -0.03284671 0.06715329 0.1671533 0.2671533
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 0.84913604 0.84913604 0.94913604 1.1491360 1.7491360
#> 2 02 0.13741257 0.13741257 0.23741257 0.4374126 1.0374126
#> 3 03 1.90121256 1.90121256 2.00121256 2.2012126 2.8012126
#> 4 04 -0.03754493 -0.03754493 0.06245507 0.2624551 0.8624551
#> 5 05 -1.23259711 -1.23259711 -1.13259711 -0.9325971 -0.3325971
#> 6 06 0.16707920 0.16707920 0.26707920 0.4670792 1.0670792
#> 7 07 0.71954641 0.71954641 0.81954641 1.0195464 1.6195464
#> 8 08 0.18677703 0.18677703 0.28677703 0.4867770 1.0867770
#> 9 09 -0.76503704 -0.76503704 -0.66503704 -0.4650370 0.1349630
#> 10 10 1.25358812 1.25358812 1.35358812 1.5535881 2.1535881