cov.sel.high.sim

The number of observations to be simulated.

The simulation setting to be used. Unconfoundedness holds given X if Setting=1. M-bias given X if Setting=2.

Setting

The number of replications to be simulated.

The type of outcome models to be used, options are <code>"Linear"</code>, <code>"Nonlinear"</code> and <code>"Binary"</code>.

Models

Function used internally by cov.sel.high to simulates example data used in H&lt;U+00E4&gt;ggstr&lt;U+00F6&gt;m (2016).

Model-free selection of covariates in high dimensions under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011) <DOI:10.1093/biomet/asr041> and VanderWeele and Shpitser (2011) <DOI:10.1111/j.1541-0420.2011.01619.x>. Confounder selection can be performed via either Markov/Bayesian networks, random forests or LASSO.

cov.sel.high.sim: Simulate Example Data for CovSelHigh

Description

Usage

Arguments

Value

References