QLearnFit: This function implements the Q-learning estimation for...

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

This function implements the Q-learning estimation for individualized treatment rule and the inference procedure based on the de-correlated score (see reference).

Description

This function implements the Q-learning estimation for individualized treatment rule and the inference procedure based on the de-correlated score (see reference).

Usage

QLearnFit(data, intercept = FALSE, standardize = TRUE)

Arguments

data	A list - list(predictor = x, treatment = trt, outcome = y)), where x is the covariate matrix, trt is 0 or 1 (1 indicates treatment), y is the outcome.
intercept	includes intercept or not
standardize	whether standardize the input covariant matrix.

Value

A list

fit

Use glmnet to fit a nigh-dimensional Q-learning.

pseudoPredictor

Augmented design matrix with covariate and treatment interations for Q-learning.

pseudoTreatment

Treatment coded in 1 and -1.

pseudoOutcome

Outcome used in the inferenc step.

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.