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 0.6272381 0.6272381 0.72723807
#> 2 02 -0.9535116 -0.9535116 -0.85351155
#> 3 03 0.6858708 0.6858708 0.78587077
#> 4 04 -0.5539804 -0.5539804 -0.45398038
#> 5 05 1.2386265 1.2386265 1.33862647
#> 6 06 -0.4417577 -0.4417577 -0.34175771
#> 7 07 -1.2461571 -1.2461571 -1.14615709
#> 8 08 1.6760899 1.6760899 1.77608987
#> 9 09 -0.1515111 -0.1515111 -0.05151108
#> 10 10 -0.9886487 -0.9886487 -0.88864873
# 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 1.1537107 1.1537107 1.2537107
#> 2 02 -2.1222649 -2.1222649 -2.0222649
#> 3 03 -0.5859338 -0.5859338 -0.4859338
#> 4 04 1.5149583 1.5149583 1.6149583
#> 5 05 0.6114122 0.6114122 0.7114122
#> 6 06 1.0332128 1.0332128 1.1332128
#> 7 07 0.4563567 0.4563567 0.5563567
#> 8 08 0.2210251 0.2210251 0.3210251
#> 9 09 -0.2440738 -0.2440738 -0.1440738
#> 10 10 0.2862861 0.2862861 0.3862861
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 0.193487898 0.29348790 0.3934879 0.4934879
#> 2 02 1.272251951 1.37225195 1.4722520 1.5722520
#> 3 03 0.327225714 0.42722571 0.5272257 0.6272257
#> 4 04 -0.005208752 0.09479125 0.1947912 0.2947912
#> 5 05 0.192387957 0.29238796 0.3923880 0.4923880
#> 6 06 -1.648590764 -1.54859076 -1.4485908 -1.3485908
#> 7 07 -1.226428848 -1.12642885 -1.0264288 -0.9264288
#> 8 08 0.177196313 0.27719631 0.3771963 0.4771963
#> 9 09 -0.800826732 -0.70082673 -0.6008267 -0.5008267
#> 10 10 2.221068887 2.32106889 2.4210689 2.5210689
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 0.45561276 0.45561276 0.55561276 0.7556128 1.3556128
#> 2 02 0.84817511 0.84817511 0.94817511 1.1481751 1.7481751
#> 3 03 0.32614138 0.32614138 0.42614138 0.6261414 1.2261414
#> 4 04 -1.57125998 -1.57125998 -1.47125998 -1.2712600 -0.6712600
#> 5 05 -0.83469660 -0.83469660 -0.73469660 -0.5346966 0.0653034
#> 6 06 -0.12578297 -0.12578297 -0.02578297 0.1742170 0.7742170
#> 7 07 0.87374116 0.87374116 0.97374116 1.1737412 1.7737412
#> 8 08 -1.07118739 -1.07118739 -0.97118739 -0.7711874 -0.1711874
#> 9 09 -0.76463954 -0.76463954 -0.66463954 -0.4646395 0.1353605
#> 10 10 -0.01387618 -0.01387618 0.08612382 0.2861238 0.8861238