mean_est: The Inverse Probability Weighted Estimator of the Marginal...

Description Usage Arguments References

View source: R/mean_est.R

Description

Estimate the marginal mean of the response when the entire population follows a treatment regime. This function implements the inverse probability weighted estimator proposed by Baqun Zhang et. al..

This function supports the mestimate function.

Usage

1
mean_est(beta, x, a, y, prob)

Arguments

beta

a vector indexing the treatment regime. It indexes a linear treatment regime:

d(x)= I{β_0 + β_1*x_1 + ... + β_k*x_k > 0}.

x

a matrix of observed covariates from the sample. Notice that we assumed the class of treatment regimes is linear. This is important that columns in x matches with beta.

a

a vector of 0s and 1s, the observed treatments from a sample

y

a vector, the observed responses from a sample

prob

a vector, the propensity scores of getting treatment 1 in the samples

References

\insertRef

zhang2012robustquantoptr


quantoptr documentation built on May 2, 2019, 4:03 p.m.