Data-driven covariate selection for nonparametric estimation of causal effects
Doris Entner, Patrik O. Hoyer, Peter Spirtes · International Conference on Artificial Intelligence and Statistics · 2013
The estimation of causal eects from nonexperimental data is a fundamental problem in many elds of science. One of the main obstacles concerns confounding by observed or latent covariates, an issue which is typically tackled by adjusting for some set of observed covariates. In this contribution, we analyze the problem of inferring whether a given variable has a causal eect on another and, if it does, inferring an adjustment set of covariates that yields a consistent and unbiased estimator of this eect, based on the (conditional) independence and dependence relationships among the observed variables. We provide two elementary rules that we show to be both sound and complete for this task, and compare the performance of a straightforward application of these rules with standard alternative procedures for selecting adjustment sets.