dml_bagging {DMLLZU} | R Documentation |
Double Machine Learning based on bagging
Description
The most famous representative of parallel ensemble learning. This method uses the self-help method to repeatedly sample from a single training set and generate several different self-help sampling training sets. Then, the self-help sampling training sets are used to fit the model and then the predicted values are obtained.
Usage
dml_bagging(y,x,d,data,sed)
Arguments
y , x , d , data , sed |
Value
y Dependent variable;
d Independent variable;
x Control variables;
sed A random seed;
data Data
Author(s)
Lixiong Yang<ylx@lzu.edu.cn>
References
Leo Breiman. (1996). Bagging Predictors. Machine Learning, 24(2), pp. 123-140. doi: 10.1023/A:1018054314350
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_bagging(y,x,d,data,sed=123)