Given the parameters, generate a dataset and return a potential outcomes schedule (science table) of synthetic potential outcomes.
make.linear.data(
n,
gamma.vec = c(1, 2, 2, 1),
gamma2.vec = NULL,
beta.vec = c(-1, -1, 1),
ideo.sd = 0,
quad.tx = FALSE,
mu.X = FALSE,
corr.X = TRUE
)make.quadradic.data(n, beta.vec = c(-1, -1, 1))
make.skew.data(n, beta.vec = c(-1, -1, 1))
List of elements of data (not data frame)
Sample size
Control outcome surface
Quadratic terms
Treatment effect surface
Ideosyncratic residual variation
Quadratic treatment effects?
Center of the X covariates (can be single number or vector of length equal to the max of the length of gamma.vec, gamma2.vec, and beta.vec)
TRUE or FALSE. Have Xs correlated or no.
make.quadradic.data: Generate dataset according to a quadratic model
make.skew.data: Generate dataset with a skew
The control outcome surface is either linear or quadratic, of the form: $$Y_i = \\gamma_0 + \\sum_{k=1}^J \\gamma_k X_{ki} + \\sum_{k=1}^{J_2} \\gamma^{(2)}_k X_{ki}^2 + \\epsilon_i$$
The individual treatment effects are similarly a linear or quadratic model.