QFitInfer: This function implements the inference for Q-learning...

In muxuanliang/ITRInference: Estimation and inference of individualized treamtent rule using pooled-and-split de-correlated score

This function implements the inference for Q-learning estimation (see reference).

Description

This function implements the inference for Q-learning estimation (see reference).

Usage

QFitInfer(qLearnFit, parallel = TRUE, indexToTest = c(1:8), intercept = TRUE)

Arguments

qLearnFit	returns of QLearnFit
parallel	whether use parallel computing; by default, FALSE.
indexToTest	indicates which coefficients to test. By default, c(1:8)
intercept	includes intercept or not

Value

p-values are the p-value for each coefficients included in indexToTest. (betaAN-1.96*sigmaAN/sqrt(sample size), betaAN+1.96*sigmaAN/sqrt(sample size)) provides the 95% CI for the coefficients.

Author(s)

Muxuan Liang <[e-mail]>

References

Muxuan Liang, Young-Geun Choi, Yang Ning, Maureen Smith, Yingqi Zhao (2020). Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score.

muxuanliang/ITRInference documentation built on Aug. 17, 2022, 6:03 p.m.