dml_neural_network: Double Machine Learning based on neural network

In DMLLZU: Double Machine Learning

Description

Each node represents a specific output function, known as the excitation function.It is a mathematical model or a computational model that imitates the structure and function of biological net. It is calculated by the connection of a large number of artificial neurons, mainly composed of nodes and the mutual connections between nodes. Each connection between two nodes represents a weighted value for the signal passing through the connection, known as the weight. The output of the network is different according to the connection mode of the network, the weight value and the excitation function.

Usage

dml_neural_network(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)

Yang Lixiong

References

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

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