dml_boosting {DMLLZU}R Documentation

Double Machine Learning based on boosting

Description

The biggest difference with other method, the trees of this method are generated sequentially. Each tree is constructed using the information of the previous generated trees. Each tree is generated according to a modified version of the original data set, and finally these trees are combined to establish a prediction model

Usage

dml_boosting(y,x,d,data,sed)

Arguments

y,x,d,data,sed

Value

y Dependent variable;

d Independent variable;

x Control variable;

sed A random seed;

data Data

Author(s)

Lixiong Yang<ylx@lzu.edu.cn>

References

Jui-Chung Yang,,Hui-Ching Chuang & Chung-Ming Kuan.(2020).Double machine learning with gradient boosting and its application to the Big N audit quality effect. Journal of Econometrics(1),. doi:10.1016/j.jeconom.2020.01.018 Victor Chernozhukov,,Denis Chetverikov,,Mert Demirer,... & James Robins.(2018).Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal(1),. doi:10.3386/w23564.

See Also

https://github.com/lixiongyang

Examples


library(ISLR)
attach(Auto)
data<- Auto
y <- data$mpg    #Dependent variable
d <- data$origin   #Independent variable
x="weight+year +horsepower"      #Control variables;

dml_boosting(y,x,d,data,sed=123)

[Package DMLLZU version 0.1.1 Index]