Model-Free Covariate Selection in High Dimensions

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) and VanderWeele and Shpitser (2011) . Confounder selection can be performed via either Markov/Bayesian networks, random forests or LASSO.