dml_ensemble_lm: dml_ensemble_lm

In DMLLZU: Double Machine Learning

Description

As an important integrated learning method, stacking consists of at least two layers of structure, including a primary learner and a secondary learner or a meta-learner used to combine the learner.Stacking first trained the primary learner from the initial data set, and then generated a new data set used to train the secondary learner, in this data set, the output of the primary learner is taken as the sample input characteristics, and the initial sample mark is still taken as the sample mark. Integrate the four basic model through linear model.

Usage

dml_ensemble_lm(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<[e-mail]>; Junchang Zhao <[e-mail]>

References

Wolpert David H.. (1992). Stacked generalization. 5(2), pp. 241-259. doi: 10.1016/S0893-6080(05)80023-1

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

help

Examples

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

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

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