R: Horvitz-Thompson estimator for the missing-data setup
mn.HT
R Documentation
Horvitz-Thompson estimator for the missing-data setup
Description
This function implements the Horvitz-Thompson estimator of the mean outcome in the presence of missing data.
Usage
mn.HT(y, tr, p, X=NULL, bal=FALSE)
Arguments
y
A vector or a matrix of outcomes with missing data.
tr
A vector of non-missing indicators (=1 if y is observed or 0 if y is missing).
p
A vector of known or fitted propensity scores.
X
The model matrix for the propensity score model, assumed to be logistic (set X=NULL if p is known or treated to be so).
bal
Logical; if TRUE, the function is used for checking balance (see the details).
Details
Variance estimation is based on asymptotic expansions, allowing for misspecification of the propensity score model.
For balance checking with bal=TRUE, the input y should correpond to the covariates for which balance is to be checked, and the output mu gives the differences between the Horvitz-Thompson estimates and the overall sample means for these covariates.
Value
mu
The estimated mean(s) or, if bal=TRUE, their differences from the overall sample means.
v
The estimated variance(s) of mu.
References
Tan, Z. (2006) "A distributional approach for causal inference using propensity scores," Journal of the American Statistical Association, 101, 1619-1637.
Tan, Z. (2010) "Bounded, efficient and doubly robust estimation with inverse weighting,"
Biometrika, 97, 661-682.