mlsanityData to 7 Oct 2026

Dynamic Structural Causal Models

arXiv 2406.01161 ↗First posted 3 Jun 2024math.ST math.PR stat.MLcited by 13

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Abstract (arXiv)

We study a specific type of SCM, called a Dynamic Structural Causal Model (DSCM), whose endogenous variables represent functions of time, which is possibly cyclic and allows for latent confounding. As a motivating use-case, we show that certain systems of Stochastic Differential Equations (SDEs) can be appropriately represented with DSCMs. An immediate consequence of this construction is a graphical Markov property for systems of SDEs. We define a time-splitting operation, allowing us to analyse the concept of local independence (a notion of continuous-time Granger (non-)causality). We also define a subsampling operation, which returns a discrete-time DSCM, and which can be used for mathematical analysis of subsampled time-series. We give suggestions how DSCMs can be used for identification of the causal effect of time-dependent interventions, and how existing constraint-based causal discovery algorithms can be applied to time-series data.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 12 of its 45 references that are in the map. First posted on arXiv 3 Jun 2024.

Builds on

12 of 45 references.

References · 12

  1. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 165
  2. 2016Foundations of Structural Causal Models with Cycles and Latent Variablescited by 134
  3. 2016From Deterministic ODEs to Dynamic Structural Causal Modelscited by 32
  4. 2017Markov Properties for Graphical Models with Cycles and Latent Variablescited by 32
  5. 2020Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cyclescited by 31
  6. 2018Causal Modeling of Dynamical Systemscited by 24
  7. 2024Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processescited by 18
  8. 2019Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Biascited by 16
  9. 2022Nonparametric Conditional Local Independence Testingcited by 7
  10. 2023Kernel-based independence tests for causal structure learning on functional datacited by 6
  11. 2020Conditional independences and causal relations implied by sets of equationscited by 5
  12. 2022Graphical criteria for the identification of marginal causal effects in continuous-time survival and event-history analysescited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 8.

13 papers.

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  1. 2024Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshotscited by 10
  2. 2026Interventional Time Series Priors for Causal Foundation Modelscited by 6
  3. 2025CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal modelscited by 5
  4. 2025Emergence of psychopathological computations in large language modelscited by 4
  5. 2026Towards Continuous-time Causal Foundation Modelscited by 3
  6. 2025Deep Koopman operator framework for causal discovery in nonlinear dynamical systemscited by 2
  7. 2026Causal Representation Meets Stochastic Modeling under Generic Geometrycited by 2
  8. 2025Causal Process Models: Reframing Dynamic Causal Graph Discovery as a Reinforcement Learning Problemcited by 1
  9. 2026A Causal DAG Prior for Synthetic Time-Series Classification Datasetscited by 1
  10. 2026Reconciling Causality and Non-Equilibrium Thermodynamics with Hamiltonian Causal Modelscited by 0
  11. 2026Temporal Causal Prior-Data Fitted Networks for Panel Data with Learned Reliability Signalscited by 0
  12. 2026Beyond Directed Acyclic Graphs: Causal Zeros and Causal Differential Equationscited by 0
1 more
  1. 2026One Intervention per Component is Enough: Towards Identifiability in Linear Stochastic Dynamics from Steady Statecited by 0