dml_bagging: Double Machine Learning based on bagging

In DMLLZU: Double Machine Learning

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<[e-mail]>

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)

DMLLZU documentation built on May 21, 2021, 5:08 p.m.