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 -1.03689560 -1.03689560 -0.93689560
#> 2 02 -0.28476301 -0.28476301 -0.18476301
#> 3 03 -1.64813864 -1.64813864 -1.54813864
#> 4 04 0.13254876 0.13254876 0.23254876
#> 5 05 1.17742017 1.17742017 1.27742017
#> 6 06 0.15552338 0.15552338 0.25552338
#> 7 07 0.32867403 0.32867403 0.42867403
#> 8 08 1.23796246 1.23796246 1.33796246
#> 9 09 0.11807125 0.11807125 0.21807125
#> 10 10 -0.09010967 -0.09010967 0.00989033
# 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 0.70061808 0.70061808 0.8006181
#> 2 02 1.06046179 1.06046179 1.1604618
#> 3 03 -2.21600760 -2.21600760 -2.1160076
#> 4 04 -0.63284750 -0.63284750 -0.5328475
#> 5 05 0.02380316 0.02380316 0.1238032
#> 6 06 -0.39373089 -0.39373089 -0.2937309
#> 7 07 -1.11150592 -1.11150592 -1.0115059
#> 8 08 0.24340115 0.24340115 0.3434011
#> 9 09 -0.64079573 -0.64079573 -0.5407957
#> 10 10 0.42395186 0.42395186 0.5239519
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 -2.15549613 -2.05549613 -1.9554961 -1.8554961
#> 2 02 -0.70445926 -0.60445926 -0.5044593 -0.4044593
#> 3 03 -0.85771516 -0.75771516 -0.6577152 -0.5577152
#> 4 04 0.26832943 0.36832943 0.4683294 0.5683294
#> 5 05 1.18665886 1.28665886 1.3866589 1.4866589
#> 6 06 0.89695772 0.99695772 1.0969577 1.1969577
#> 7 07 -0.06630224 0.03369776 0.1336978 0.2336978
#> 8 08 1.59087566 1.69087566 1.7908757 1.8908757
#> 9 09 0.03203081 0.13203081 0.2320308 0.3320308
#> 10 10 -0.04787739 0.05212261 0.1521226 0.2521226
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 -0.7326153 -0.7326153 -0.6326153 -0.43261530 0.1673847
#> 2 02 -0.5147579 -0.5147579 -0.4147579 -0.21475792 0.3852421
#> 3 03 -1.4046127 -1.4046127 -1.3046127 -1.10461270 -0.5046127
#> 4 04 0.3070250 0.3070250 0.4070250 0.60702500 1.2070250
#> 5 05 1.2171252 1.2171252 1.3171252 1.51712521 2.1171252
#> 6 06 -1.3433557 -1.3433557 -1.2433557 -1.04335571 -0.4433557
#> 7 07 0.6043685 0.6043685 0.7043685 0.90436855 1.5043685
#> 8 08 0.3697197 0.3697197 0.4697197 0.66971970 1.2697197
#> 9 09 0.8543749 0.8543749 0.9543749 1.15437491 1.7543749
#> 10 10 -0.3640760 -0.3640760 -0.2640760 -0.06407601 0.5359240