mean_est {quantoptr}

The Inverse Probability Weighted Estimator of the Marginal Mean Given a Specific Treatment Regime

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

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

Arguments

beta

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

d(x)= I\{\beta_0 + \beta_1 x_1 + ... + \beta_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

Zhang B, Tsiatis AA, Laber EB and Davidian M (2012). “A robust method for estimating optimal treatment regimes.” Biometrics, 68(4), pp. 1010–1018.