make_irm_data: Generates data from a interactive regression (IRM) model.

In DoubleML: Double Machine Learning in R

Generates data from a interactive regression (IRM) model.

Description

Generates data from a interactive regression (IRM) model. The data generating process is defined as

d_i = 1\left\lbrace \frac{\exp(c_d x_i' \beta)}{1+\exp(c_d x_i' \beta)} > v_i \right\rbrace,

y_i = \theta d_i + c_y x_i' \beta d_i + \zeta_i,

with v_i \sim \mathcal{U}(0,1), \zeta_i \sim \mathcal{N}(0,1) and covariates x_i \sim \mathcal{N}(0, \Sigma), where \Sigma is a matrix with entries \Sigma_{kj} = 0.5^{|j-k|}. \beta is a dim_x-vector with entries \beta_j = \frac{1}{j^2} and the constancts c_y and c_d are given by

c_y = \sqrt{\frac{R_y^2}{(1-R_y^2) \beta' \Sigma \beta}},

c_d = \sqrt{\frac{(\pi^2 /3) R_d^2}{(1-R_d^2) \beta' \Sigma \beta}}.

The data generating process is inspired by a process used in the simulation experiment (see Appendix P) of Belloni et al. (2017).

Usage

make_irm_data(
  n_obs = 500,
  dim_x = 20,
  theta = 0,
  R2_d = 0.5,
  R2_y = 0.5,
  return_type = "DoubleMLData"
)

Arguments

n_obs	(integer(1)) The number of observations to simulate.
dim_x	(integer(1)) The number of covariates.
theta	(numeric(1)) The value of the causal parameter.
R2_d	(numeric(1)) The value of the parameter R_d^2.
R2_y	(numeric(1)) The value of the parameter R_y^2.
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, d and z is returned. Every entry in the list is a matrix() object. Default is "DoubleMLData".

References

Belloni, A., Chernozhukov, V., Fernández-Val, I. and Hansen, C. (2017). Program Evaluation and Causal Inference With High-Dimensional Data. Econometrica, 85: 233-298.

DoubleML documentation built on April 1, 2023, 12:16 a.m.