dml_boosting: Double Machine Learning based on boosting
In DMLLZU: Double Machine Learning

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/DML.R

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

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

y,x,d,data,sed

y Dependent variable;

d Independent variable;

x Control variable;

sed A random seed;

data Data

Lixiong Yang<ylx@lzu.edu.cn>

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.

https://github.com/lixiongyang

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)