# make_plr_CCDDHNR2018: Generates data from a partially linear regression model used... In DoubleML: Double Machine Learning in R

## Description

Generates data from a partially linear regression model used in Chernozhukov et al. (2018) for Figure 1. The data generating process is defined as

d_i = m_0(x_i) + s_1 v_i,

y_i = α d_i + g_0(x_i) + s_2 ζ_i,

with v_i \sim \mathcal{N}(0,1) and ζ_i \sim \mathcal{N}(0,1),. The covariates are distributed as x_i \sim \mathcal{N}(0, Σ), where Σ is a matrix with entries Σ_{kj} = 0.7^{|j-k|}. The nuisance functions are given by

m_0(x_i) = a_0 x_{i,1} + a_1 \frac{\exp(x_{i,3})}{1+\exp(x_{i,3})},

g_0(x_i) = b_0 \frac{\exp(x_{i,1})}{1+\exp(x_{i,1})} + b_1 x_{i,3},

with a_0=1, a_1=0.25, s_1=1, b_0=1, b_1=0.25, s_2=1.

## Usage

 1 2 3 4 5 6 make_plr_CCDDHNR2018( n_obs = 500, dim_x = 20, alpha = 0.5, return_type = "DoubleMLData" ) 

## Arguments

 n_obs (integer(1)) The number of observations to simulate. dim_x (integer(1)) The number of covariates. alpha (numeric(1)) The value of the causal parameter. return_type (character(1)) If "DoubleMLData", returns a DoubleMLData object. If "data.frame" returns a data.frame(). If "data.table" returns a data.table(). If "matrix" a named list() with entries X, y and d is returned. Every entry in the list is a matrix() object. Default is "DoubleMLData".

## Value

A data object according to the choice of return_type.

## References

Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W. and Robins, J. (2018), Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21: C1-C68. doi: 10.1111/ectj.12097.

DoubleML documentation built on Oct. 26, 2021, 5:06 p.m.