Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past

arXiv:2203.06056v2 Announce Type: replace-cross
Abstract: Instrumental variable (IV) regression relies on instruments to infer causal effects from observational data with unobserved confounding. We consider IV regression in time series models, such as vector auto-regressive (VAR) processes. Direct applications of i.i.d. techniques are generally inconsistent as they do not correctly adjust for dependencies in the past. In this paper, we outline the difficulties that arise due to time structure and propose methodology for constructing identifying equations that can be used for …

abstract applications arxiv auto causal confounding data effects processes regression series stat.me stat.ml time series type vector