Description Usage Arguments Details Value References Examples
This function implements model-assisted inference for average treatment effects, using non-regularized calibrated estimation.
1 |
y |
An n x 1 vector of observed outcomes. |
tr |
An n x 1 vector of treatment indicators (=1 if treated or 0 if untreated). |
x |
An n x p matix of covariates, used in both propensity score and outcome regression models. |
ploss |
A loss function used in propensity score estimation (either "ml" or "cal"). |
yloss |
A loss function used in outcome regression (either "gaus" for continuous outcomes or "ml" for binary outcomes). |
off |
A 2 x 1 vector of offset values (e.g., the true values in simulations) used to calculate the z-statistics from augmented IPW estimation. |
For calibrated estimation, two sets of propensity scores are separately estimated for the untreated and treated as discussed in Tan (2020a, 2020b).
See also Details for mn.nreg
.
ps |
A list containing the results from fitting the propensity score model by |
mfp |
An n x 2 matrix of fitted propensity scores for untreated (first column) and treated (second column). |
or |
A list containing the results from fitting the outcome regression model by |
mfo |
An n x 2 matrix of fitted values from outcome regression, for untreated (first column) and treated (second column). |
est |
A list containing the results from augmented IPW estimation by |
Tan, Z. (2020a) Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data, Biometrika, 107, 137<e2><80><93>158.
Tan, Z. (2020b) Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data, Annals of Statistics, 48, 811<e2><80><93>837.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | data(simu.data)
n <- dim(simu.data)[1]
p <- dim(simu.data)[2]-2
y <- simu.data[,1]
tr <- simu.data[,2]
x <- simu.data[,2+1:p]
x <- scale(x)
# include only 10 covariates
x2 <- x[,1:10]
ate.cal <- ate.nreg(y, tr, x2, ploss="cal", yloss="gaus")
matrix(unlist(ate.cal$est), ncol=2, byrow=TRUE,
dimnames=list(c("one", "ipw", "or", "est", "var", "ze",
"diff.est", "diff.var", "diff.ze"), c("untreated", "treated")))
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