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

• URL: http://arxiv.org/abs/2203.06056v3
• Date: Fri, 19 Jul 2024 20:48:15 GMT
• Title: Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past
• Authors: Nikolaj Thams, Rikke Søndergaard, Sebastian Weichwald, Jonas Peters,
• Abstract summary: 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. We provide methods, prove their consistency, and show how the inferred causal effect can be used for distribution generalization.
• Score: 12.49477539101379
• License: http://creativecommons.org/licenses/by/4.0/
• 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 consistent parametric estimation of causal effects in time series data. One method uses extra nuisance covariates to obtain identifiability (an idea that can be of interest even in the i.i.d. case). We further propose a graph marginalization framework that allows us to apply nuisance IV and other IV methods in a principled way to time series. Our methods make use of a version of the global Markov property, which we prove holds for VAR(p) processes. For VAR(1) processes, we prove identifiability conditions that relate to Jordan forms and are different from the well-known rank conditions in the i.i.d. case (they do not require as many instruments as covariates, for example). We provide methods, prove their consistency, and show how the inferred causal effect can be used for distribution generalization. Simulation experiments corroborate our theoretical results. We provide ready-to-use Python code.

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