dml_random_forest: Double Machine Learning based on random forest

In DMLLZU: Double Machine Learning

Description

To establish a series of decision tree, the difference is the this method for each division point considering the decision tree, should be chosen from among all variables contain random sample with some of the variables as candidate variables, the explanatory variables can only be used in the split point from the selected part of the explanation variable selection, and then to make predictions.

Usage

dml_random_forest(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]>

References

Leo Breiman. (2001). Random Forests. Machine Learning, 45(1), pp. 5-32. doi:10.1023/A:1010933404324

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.

Examples

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

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

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