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Ancestor regression in structural vector autoregressive models

Author

Listed:
  • 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)

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.

Suggested Citation

  • Schultheiss Christoph & Ulmer Markus & Bühlmann Peter, 2025. "Ancestor regression in structural vector autoregressive models," Journal of Causal Inference, De Gruyter, vol. 13(1), pages 1-25.
    • Handle: RePEc:bpj:causin:v:13:y:2025:i:1:p:25:n:1002
    DOI: 10.1515/jci-2024-0011
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    References listed on IDEAS

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    1. James Davidson & Robert de Jong, 1997. "Strong laws of large numbers for dependent heterogeneous processes: a synthesis of recent and new results," Econometric Reviews, Taylor & Francis Journals, vol. 16(3), pages 251-279.
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