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 0.4930602 0.4930602 0.5930602
#> 2 02 1.3366320 1.3366320 1.4366320
#> 3 03 0.8886001 0.8886001 0.9886001
#> 4 04 -2.0938406 -2.0938406 -1.9938406
#> 5 05 1.4189190 1.4189190 1.5189190
#> 6 06 1.0430426 1.0430426 1.1430426
#> 7 07 -0.3119563 -0.3119563 -0.2119563
#> 8 08 -1.0932664 -1.0932664 -0.9932664
#> 9 09 -1.2231422 -1.2231422 -1.1231422
#> 10 10 -2.1206555 -2.1206555 -2.0206555
# 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 0.09763563 0.09763563 0.19763563
#> 2 02 0.18129160 0.18129160 0.28129160
#> 3 03 0.07425098 0.07425098 0.17425098
#> 4 04 -0.51311933 -0.51311933 -0.41311933
#> 5 05 0.67200311 0.67200311 0.77200311
#> 6 06 -1.69245885 -1.69245885 -1.59245885
#> 7 07 1.46719145 1.46719145 1.56719145
#> 8 08 -2.14625864 -2.14625864 -2.04625864
#> 9 09 0.66513070 0.66513070 0.76513070
#> 10 10 -0.12509188 -0.12509188 -0.02509188
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 2.46837585 2.56837585 2.66837585 2.76837585
#> 2 02 -0.05503008 0.04496992 0.14496992 0.24496992
#> 3 03 -0.38770988 -0.28770988 -0.18770988 -0.08770988
#> 4 04 0.44484089 0.54484089 0.64484089 0.74484089
#> 5 05 -0.14559963 -0.04559963 0.05440037 0.15440037
#> 6 06 0.59644784 0.69644784 0.79644784 0.89644784
#> 7 07 -0.79597627 -0.69597627 -0.59597627 -0.49597627
#> 8 08 2.03671031 2.13671031 2.23671031 2.33671031
#> 9 09 -0.44919350 -0.34919350 -0.24919350 -0.14919350
#> 10 10 0.17858121 0.27858121 0.37858121 0.47858121
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 0.5923451 0.5923451 0.69234510 0.8923451 1.49234510
#> 2 02 -0.1276024 -0.1276024 -0.02760236 0.1723976 0.77239764
#> 3 03 -0.7061509 -0.7061509 -0.60615093 -0.4061509 0.19384907
#> 4 04 -0.8151740 -0.8151740 -0.71517397 -0.5151740 0.08482603
#> 5 05 0.1237721 0.1237721 0.22377209 0.4237721 1.02377209
#> 6 06 1.1222136 1.1222136 1.22221358 1.4222136 2.02221358
#> 7 07 0.1028767 0.1028767 0.20287669 0.4028767 1.00287669
#> 8 08 1.3326670 1.3326670 1.43266696 1.6326670 2.23266696
#> 9 09 1.0134507 1.0134507 1.11345069 1.3134507 1.91345069
#> 10 10 0.2733610 0.2733610 0.37336099 0.5733610 1.17336099