late.nreg {RCAL} | R Documentation |
This function implements model-assisted inference for local average treatment effects, using non-regularized calibrated estimation.
late.nreg(y, tr, iv, fx, gx, hx, arm = 2, d1 = NULL, d2 = NULL,
ploss = "cal", yloss = "gaus", off = NULL)
y |
An |
tr |
An |
iv |
An |
fx |
An |
gx |
An |
hx |
An |
arm |
An integer 0, 1 or 2 indicating whether |
d1 |
Degree of truncated polynomials of fitted values from treatment regression to be included as regressors in the outcome regression (NULL: no adjustment, 0: piecewise constant, 1: piecewise linear etc..). |
d2 |
Number of knots of fitted values from treatment regression to be included as regressors in the outcome regression, with knots specified as the |
ploss |
A loss function used in instrument propensity score estimation (either "ml" for likelihood estimation or "cal" for calibrated estimation). |
yloss |
A loss function used in outcome regression (either "gaus" for continuous outcomes or "ml" for binary outcomes). |
off |
A |
For ploss="cal", calibrated estimation of the instrument propensity score (IPS) and weighted likelihood estimation of the treatment and outcome regression models are performed, similarly as in Sun and Tan (2020), but without regularization.
See also Details for mn.nreg
.
ips |
A list containing the results from fitting the instrument propensity score models by |
mfp |
An |
tps |
A list containing the results from fitting the treatment regression models by |
mft |
An |
or |
A list containing the results from fitting the outcome regression models by |
mfo |
An |
est |
A list containing the results from augmented IPW estimation by |
Tan, Z. (2006) Regression and weighting methods for causal inference using instrumental variables, Journal of the American Statistical Association, 101, 1607–1618.
Sun, B. and Tan, Z. (2020) High-dimensional model-assisted inference for local average treatment effects with instrumental variables, arXiv:2009.09286.
data(simu.iv.data)
n <- dim(simu.iv.data)[1]
p <- dim(simu.iv.data)[2]-3
y <- simu.iv.data[,1]
tr <- simu.iv.data[,2]
iv <- simu.iv.data[,3]
x <- simu.iv.data[,3+1:p]
x <- scale(x)
# include only 10 covariates
x2 <- x[,1:10]
late.cal <- late.nreg(y, tr, iv, fx=x2, gx=x2, hx=x2, arm=2, d1=1, d2=3,
ploss="cal", yloss="gaus")
matrix(unlist(late.cal$est), ncol=2, byrow=TRUE,
dimnames=list(c("ipw", "or", "est", "var", "ze",
"late.est", "late.var", "late.ze"), c("theta1", "theta0")))