# make_iivm_data: Generates data from a interactive IV regression (IIVM) model. In DoubleML: Double Machine Learning in R

## Description

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

d_i = 1≤ft\lbrace α_x Z + v_i > 0 \right\rbrace,

y_i = θ d_i + x_i' β + u_i,

Z \sim \textstyle{Bernoulli} (0.5) and

≤ft(\begin{array}{c} u_i \\ v_i \end{array} \right) \sim \mathcal{N}≤ft(0, ≤ft(\begin{array}{cc} 1 & 0.3 \\ 0.3 & 1 \end{array} \right) \right).

The covariates :x_i \sim \mathcal{N}(0, Σ), where Σ is a matrix with entries Σ_{kj} = 0.5^{|j-k|} and β is a dim_x-vector with entries β_j=\frac{1}{j^2}.

The data generating process is inspired by a process used in the simulation experiment of Farbmacher, Gruber and Klaaßen (2020).

## Usage

 1 2 3 4 5 6 7 make_iivm_data( n_obs = 500, dim_x = 20, theta = 1, alpha_x = 0.2, 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. alpha_x (numeric(1)) The value of the parameter α_x. 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

Farbmacher, H., Guber, R. and Klaaßen, S. (2020). Instrument Validity Tests with Causal Forests. MEA Discussion Paper No. 13-2020. Available at SSRN:doi: 10.2139/ssrn.3619201.

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