| generate_causal_survival_data {grf} | R Documentation |
The following DGPs are available for benchmarking purposes, T is the failure time and C the censoring time:
"simple1": T = X1*eps + W, C ~ U(0, 2) where eps ~ Exp(1) and Y.max = 1.
"type1": T is drawn from an accelerated failure time model and C from a Cox model (scenario 1 in https://arxiv.org/abs/2001.09887)
"type2": T is drawn from a proportional hazard model and C from a accelerated failure time (scenario 2 in https://arxiv.org/abs/2001.09887)
"type3": T and C are drawn from a Poisson distribution (scenario 3 in https://arxiv.org/abs/2001.09887)
"type4": T and C are drawn from a Poisson distribution (scenario 4 in https://arxiv.org/abs/2001.09887)
"type5": is similar to "type2" but with censoring generated from an accelerated failure time model.
generate_causal_survival_data(
n,
p,
Y.max = NULL,
X = NULL,
n.mc = 10000,
dgp = c("simple1", "type1", "type2", "type3", "type4", "type5")
)
n |
The number of samples. |
p |
The number of covariates. |
Y.max |
The maximum follow-up time (optional). |
X |
The covariates (optional). |
n.mc |
The number of monte carlo draws to estimate the treatment effect with. Default is 10000. |
dgp |
The type of DGP. |
A list with entries: 'X': the covariates, 'Y': the event times, 'W': the treatment indicator, 'D': the censoring indicator, 'cate': the treatment effect estimated by monte carlo, 'cate.sign': the true sign of the cate for ITR comparison, 'dgp': the dgp name, 'Y.max': the maximum follow-up time.
# Generate data n <- 1000 p <- 5 data <- generate_causal_survival_data(n, p) # Get true CATE on a test set X.test <- matrix(seq(0, 1, length.out = 5), 5, p) cate.test <- generate_causal_survival_data(n, p, X = X.test)$cate