  In observational studies, the causal effect of a treatment may be confounded
with variables that are related to both the treatment and the outcome of
interest. In order to identify a causal effect, such studies often rely on the
unconfoundedness assumption, i.e., that all confounding variables are observed.
The choice of covariates to control for, which is primarily based on subject
matter knowledge, may result in a large covariate vector in the attempt to
ensure that unconfoundedness holds. However, including redundant covariates can
affect bias and efficiency of nonparametric causal effect estimators, e.g., due
to the curse of dimensionality. Data-driven algorithms for the selection of
sufficient covariate subsets are investigated. Under the assumption of
unconfoundedness the algorithms search for minimal subsets of the covariate
vector. Based, e.g., on the framework of sufficient dimension reduction or
kernel smoothing, the algorithms perform a backward elimination procedure
assessing the significance of each covariate. Their performance is evaluated in
simulations and an application using data from the Swedish Childhood Diabetes
Register is also presented.
