Ancestor regression in structural vector autoregressive models

Schultheiss Christoph (), Ulmer Markus () and Bühlmann Peter ()
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Schultheiss Christoph: Seminar for Statistics, D-MATH (Department for Mathematics), ETH Zürich, Switzerland
Ulmer Markus: Seminar for Statistics, D-MATH (Department for Mathematics), ETH Zürich, Switzerland
Bühlmann Peter: Seminar for Statistics, D-MATH (Department for Mathematics), ETH Zürich, Switzerland

Journal of Causal Inference, 2025, vol. 13, issue 1, 25

Abstract: We present a new method for causal discovery in linear structural vector autoregressive models. We adapt an idea designed for independent observations to the case of time series while retaining its favorable properties, i.e., explicit error control for false causal discovery, at least asymptotically. We apply our method to several real-world bivariate time series datasets and discuss its findings that mostly agree with common understanding. The arrow of time in a model can be interpreted as background knowledge on possible causal mechanisms. Hence, our ideas could be extended to incorporating different background knowledge, even for independent observations.

Keywords: time series data; causal discovery; error guarantees (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:13:y:2025:i:1:p:25:n:1002

DOI: 10.1515/jci-2024-0011

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