General Control Functions for Causal Effect Estimation from Instrumental Variables.

ABSTRACT: Causal effect estimation relies on separating the variation in the outcome into parts due to the treatment and due to the confounders. To achieve this separation, practitioners often use external sources of randomness that only influence the treatment called instrumental variables (IVs). We study variables constructed from treatment and IV that help estimate effects, called control functions. We characterize general control functions for effect estimation in a meta-identification result. Then, we show that structural assumptions on the treatment process allow the construction of general control functions, thereby guaranteeing identification. To construct general control functions and estimate effects, we develop the general control function method (GCFN). GCFN's first stage called variatio

SUBMITTER: Puli A 

PROVIDER: S-EPMC8096518 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature