Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributions

• URL: http://arxiv.org/abs/2508.21742v1
• Date: Fri, 29 Aug 2025 16:08:35 GMT
• Title: Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributions
• Authors: Timothée Loranchet, Charles K. Assaad,
• Abstract summary: We present conditions that guarantee the orientability of micro-level edges between temporal variables.<n>We highlight the value of incorporating expert knowledge to improve causal inference from observational time series data.
• Score: 5.500249707065663
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding causal relations between temporal variables is a central challenge in time series analysis, particularly when the full causal structure is unknown. Even when the full causal structure cannot be fully specified, experts often succeed in providing a high-level abstraction of the causal graph, known as a summary causal graph, which captures the main causal relations between different time series while abstracting away micro-level details. In this work, we present conditions that guarantee the orientability of micro-level edges between temporal variables given the background knowledge encoded in a summary causal graph and assuming having access to a faithful and causally sufficient distribution with respect to the true unknown graph. Our results provide theoretical guarantees for edge orientation at the micro-level, even in the presence of cycles or bidirected edges at the macro-level. These findings offer practical guidance for leveraging SCGs to inform causal discovery in complex temporal systems and highlight the value of incorporating expert knowledge to improve causal inference from observational time series data.

Related papers

• DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data [2.681371732194511]
  Time series in domains such as finance, climate science, and healthcare often exhibit long-term trends, seasonal patterns, and short-term fluctuations.<n>Existing causal discovery methods operate on raw observations, making them vulnerable to spurious edges and misattributed temporal dependencies.<n>We introduce a decomposition-based causal discovery framework that separates each time series into trend, seasonal, and residual components.
  arXiv  Detail & Related papers  (2026-02-01T20:45:05Z)
• Flow-Based Non-stationary Temporal Regime Causal Structure Learning [49.77103348208835]
  We introduce FANTOM, a unified framework for causal discovery.<n>It handles non stationary processes along with non Gaussian and heteroscedastic noises.<n>It simultaneously infers the number of regimes and their corresponding indices and learns each regime's Directed Acyclic Graph.
  arXiv  Detail & Related papers  (2025-06-20T15:12:43Z)
• New Rules for Causal Identification with Background Knowledge [59.733125324672656]
  We propose two novel rules for incorporating BK, which offer a new perspective to the open problem. We show that these rules are applicable in some typical causality tasks, such as determining the set of possible causal effects with observational data.
  arXiv  Detail & Related papers  (2024-07-21T20:21:21Z)
• Identifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confounding [0.9208007322096533]
  Partially specified causal graphs (SCGs) provide a simplified representation of causal relations between time series when working spacio-temporal data.<n>Unlike fully specified causal graphs, SCGs can contain cycles, which complicate their analysis and interpretation.<n>In this paper, we first clearly distinguish between macro conditional independencies and micro conditional independencies and between macro total effects and micro total effects.
  arXiv  Detail & Related papers  (2024-07-10T16:03:04Z)
• Towards identifiability of micro total effects in summary causal graphs with latent confounding: extension of the front-door criterion [1.0878040851638]
  Researchers often rely on causal graphs to determine whether these effects can be identified from observational data.<n>This paper addresses the challenge of identifying total effects using a specific and well-known partially specified graph in dynamic systems.
  arXiv  Detail & Related papers  (2024-06-09T14:43:06Z)
• On the Identification of Temporally Causal Representation with Instantaneous Dependence [50.14432597910128]
  Temporally causal representation learning aims to identify the latent causal process from time series observations. Most methods require the assumption that the latent causal processes do not have instantaneous relations. We propose an textbfIDentification framework for instantanetextbfOus textbfLatent dynamics.
  arXiv  Detail & Related papers  (2024-05-24T08:08:05Z)
• Causal Temporal Regime Structure Learning [49.77103348208835]
  We present CASTOR, a novel method that concurrently learns the Directed Acyclic Graph (DAG) for each regime.<n>We establish the identifiability of the regimes and DAGs within our framework.<n>Experiments show that CASTOR consistently outperforms existing causal discovery models.
  arXiv  Detail & Related papers  (2023-11-02T17:26:49Z)
• Effect Identification in Cluster Causal Diagrams [51.42809552422494]
  We introduce a new type of graphical model called cluster causal diagrams (for short, C-DAGs) C-DAGs allow for the partial specification of relationships among variables based on limited prior knowledge. We develop the foundations and machinery for valid causal inferences over C-DAGs.
  arXiv  Detail & Related papers  (2022-02-22T21:27:31Z)
• Path Signature Area-Based Causal Discovery in Coupled Time Series [0.0]
  We propose the application of confidence sequences to analyze the significance of the magnitude of the signed area between two variables. This approach provides a new way to define the confidence of a causal link existing between two time series.
  arXiv  Detail & Related papers  (2021-10-23T19:57:22Z)
• An Empirical Study: Extensive Deep Temporal Point Process [61.14164208094238]
  We first review recent research emphasis and difficulties in modeling asynchronous event sequences with deep temporal point process.<n>We propose a Granger causality discovery framework for exploiting the relations among multi-types of events.
  arXiv  Detail & Related papers  (2021-10-19T10:15:00Z)
• Consistency of mechanistic causal discovery in continuous-time using Neural ODEs [85.7910042199734]
  We consider causal discovery in continuous-time for the study of dynamical systems. We propose a causal discovery algorithm based on penalized Neural ODEs.
  arXiv  Detail & Related papers  (2021-05-06T08:48:02Z)
• Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confounding [101.35070661471124]
  We show that unobserved confounding leaves a characteristic footprint in the observed data distribution that allows for disentangling spurious and causal effects. We propose an adjusted score-based causal discovery algorithm that may be implemented with general-purpose solvers and scales to high-dimensional problems.
  arXiv  Detail & Related papers  (2021-03-28T11:07:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.