| tau {drcarlate} | R Documentation |
Compute Estimated LATE
Description
Computes the estimated LATE in Jiang et al. (2022).
Usage
tau(muY1, muY0, muD1, muD0, A, S, Y, D, stratnum = NULL)
Arguments
muY1 |
A nx1 vector of hat{mu}^Y(A=1)s. |
muY0 |
A nx1 vector of hat{mu}^Y(A=0)s. |
muD1 |
A nx1 vector of hat{mu}^D(A=1)s. |
muD0 |
A nx1 vector of hat{mu}^D(A=0)s. |
A |
A nx1 vector. Each of its elements is the treatment assignment of the corresponding observation. |
S |
A nx1 vector. Each of its elements is the stratum of corresponding observation. |
Y |
A nx1 vector. Each of its elements is the observed outcome of interest of corresponding observation. |
D |
A nx1 vector. Each of its elements is is a binary random variable indicating whether the individual i received treatment (Di = 1) or not (Di = 0) in the actual study. |
stratnum |
A nx1 vector about the unique strata numbers, the default value is NULL. |
Value
A scalar. LATE estimate.
References
Jiang L, Linton O B, Tang H, Zhang Y. Improving estimation efficiency via regression-adjustment in covariate-adaptive randomizations with imperfect compliance [J]. 2022.
Examples
DGP <- FuncDGP(dgptype = 1, rndflag = 1, n = 200, g = 4, pi = c(0.5, 0.5, 0.5, 0.5))
muY1 <- DGP[["Y1"]]
muY0 <- DGP[["Y0"]]
muD1 <- DGP[["D1"]]
muD0 <- DGP[["D0"]]
A <- DGP[["A"]]
S <- DGP[["S"]]
Y <- DGP[["Y"]]
D <- DGP[["D"]]
tau(muY1, muY0, muD1, muD0, A, S, Y, D)