A New Criterion for Confounder Selection

• Activity Number: 330
• Type: Invited
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: SSC
• Author(s): Tyler VanderWeele* and Ilya Shpitser
• Companies: Harvard University and University of Southampton
• Keywords: Causal inference ; Confounding ; Ignorability ; Causal Diagrams ; Treatment Effects

Abstract

We propose a new criterion for the control of confounding when the underlying causal structure is unknown and only limited knowledge is available. We assume all covariates being considered are pretreatment variables and that for each covariate it is known (i) whether the covariate is a cause of treatment, and (ii) whether the covariate is a cause of the outcome. The causal relationships the covariates have with one another is assumed unknown. We propose that control be made for any covariate that is either a cause of treatment or a cause of the outcome or both. We show that irrespective of the actual underlying causal structure, if any subset of the observed covariates suffices to control for confounding (i.e. for conditional ignorability) then the set of covariates chosen by our criterion will also suffice. We show that other, commonly used, criteria for confounding control do not have this property.