mlsanityData to 7 Oct 2026

From Deterministic ODEs to Dynamic Structural Causal Models

arXiv 1608.08028 ↗First posted 29 Aug 2016cs.AIcited by 32

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Abstract (arXiv)

Structural Causal Models are widely used in causal modelling, but how they relate to other modelling tools is poorly understood. In this paper we provide a novel perspective on the relationship between Ordinary Differential Equations and Structural Causal Models. We show how, under certain conditions, the asymptotic behaviour of an Ordinary Differential Equation under non-constant interventions can be modelled using Dynamic Structural Causal Models. In contrast to earlier work, we study not only the effect of interventions on equilibrium states; rather, we model asymptotic behaviour that is dynamic under interventions that vary in time, and include as a special case the study of static equilibria.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Aug 2016.

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Citing papers per year: 2021: 6; 2022: 4; 2023: 10; 2024: 3; 2025: 6; 2026: 3.

32 papers.

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  1. 2021Towards Causal Representation Learningcited by 207
  2. 2022Diffusion Causal Models for Counterfactual Estimationcited by 87
  3. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  4. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  5. 2021Neural graphical modelling in continuous-time: consistency guarantees and algorithmscited by 42
  6. 2022From Statistical to Causal Learningcited by 40
  7. 2023Discovering Causal Relations and Equations from Datacited by 33
  8. 2021Causal Navigation by Continuous-time Neural Networkscited by 23
  9. 2021Dynamic Causal Bayesian Optimizationcited by 17
  10. 2023Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounderscited by 17
  11. 2021Beyond Predictions in Neural ODEs: Identification and Interventionscited by 15
  12. 2023Identifiability of total effects from abstractions of time series causal graphscited by 13
20 more
  1. 2024Dynamic Structural Causal Modelscited by 13
  2. 2023Identifiability of Direct Effects from Summary Causal Graphscited by 12
  3. 2022Towards a Grounded Theory of Causation for Embodied AIcited by 9
  4. 2022Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observationscited by 8
  5. 2023A Measure-Theoretic Axiomatisation of Causalitycited by 8
  6. 2023Generator Identification for Linear SDEs with Additive and Multiplicative Noisecited by 7
  7. 2023MetaPhysiCa: OOD Robustness in Physics-informed Machine Learningcited by 6
  8. 2024Identifiability Analysis of Linear ODE Systems with Hidden Confounderscited by 5
  9. 2025CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal modelscited by 5
  10. 2021Causality and independence in perfectly adapted dynamical systemscited by 4
  11. 2023Double Machine Learning Based Structure Identification from Temporal Datacited by 3
  12. 2025Deep Koopman operator framework for causal discovery in nonlinear dynamical systemscited by 2
  13. 2025Identifiability by common backdoor in summary causal graphs of time seriescited by 2
  14. 2025Causal Structure Learning for Dynamical Systems with Theoretical Score Analysiscited by 2
  15. 2025When Counterfactual Reasoning Fails: Chaos and Real-World Complexitycited by 1
  16. 2025Causal Process Models: Reframing Dynamic Causal Graph Discovery as a Reinforcement Learning Problemcited by 1
  17. 2024A Dynamical View of the Question of Whycited by 0
  18. 2026M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Datacited by 0
  19. 2026Generative Modeling with Flux Matchingcited by 0
  20. 2026Reconciling Causality and Non-Equilibrium Thermodynamics with Hamiltonian Causal Modelscited by 0