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ITSY: Causal Discovery From Irregular Time-Series Data

arXiv 2609.23516 ↗First posted 20 Sep 2026cs.LGcited by 0

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Abstract (arXiv)

Structural causal models for time series recover contemporaneous and lagged effects, but most methods require complete observation windows and become misspecified when samples are missing. We introduce ITSY, the first continuous-optimization method for causal discovery from irregular time series under a linear model. ITSY reformulates the structural equation so that prediction uses the nearest available history rather than the possibly missing current slice, and jointly imputes missing values while learning both graphs. A weighted reconstruction objective corrects the noise transformation induced by this reformulation. Across synthetic regimes varying missingness, scale, graph density, and noise, and on a real world benchmark, ITSY consistently improves graph recovery over representative SCM-based baselines, demonstrating the effectiveness of the proposed method. The results establish a focused solution for irregular linear first-order dynamics and clarify the assumptions required for nonlinear or higher-order extensions.

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References · 24

  1. 2018Neural Ordinary Differential Equationscited by 3,779
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  11. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  12. 2021Neural graphical modelling in continuous-time: consistency guarantees and algorithmscited by 42
12 more
  1. 2023CUTS+: High-dimensional Causal Discovery from Irregular Time-seriescited by 41
  2. 2023Structure Learning with Continuous Optimization: A Sober Look and Beyondcited by 36
  3. 2021NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledgecited by 33
  4. 2023CUTS: Neural Causal Discovery from Irregular Time-Series Datacited by 33
  5. 2022Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noisecited by 30
  6. 2020Reconstructing regime-dependent causal relationships from observational time seriescited by 28
  7. 2022MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Modelscited by 22
  8. 2023Neural Structure Learning with Stochastic Differential Equationscited by 14
  9. 2023Causal Discovery from Subsampled Time Series with Proxy Variablescited by 8
  10. 2023Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithmscited by 8
  11. 2025Causal Discovery for Irregularly Time Series with Consistency Guaranteescited by 2
  12. 2025Causal Structure Learning for Dynamical Systems with Theoretical Score Analysiscited by 2

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