Identifiability by common backdoor in summary causal graphs of time series

• URL: http://arxiv.org/abs/2506.14862v1
• Date: Tue, 17 Jun 2025 17:37:27 GMT
• Title: Identifiability by common backdoor in summary causal graphs of time series
• Authors: Clément Yvernes, Charles K. Assaad, Emilie Devijver, Eric Gaussier,
• Abstract summary: The identifiability problem for interventions aims at assessing whether the total effect of some given interventions can be written with a do-free formula, and thus be computed from observational data only.<n>We study this problem, considering multiple interventions and multiple effects, in the context of time series when only abstractions of the true causal graph in the form of summary causal graphs are available.<n>We focus in this study on identifiability by a common backdoor set, and establish, for time series with and without consistency throughout time, conditions under which such a set exists.
• Score: 2.737398629157413
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The identifiability problem for interventions aims at assessing whether the total effect of some given interventions can be written with a do-free formula, and thus be computed from observational data only. We study this problem, considering multiple interventions and multiple effects, in the context of time series when only abstractions of the true causal graph in the form of summary causal graphs are available. We focus in this study on identifiability by a common backdoor set, and establish, for time series with and without consistency throughout time, conditions under which such a set exists. We also provide algorithms of limited complexity to decide whether the problem is identifiable or not.

Related papers

• 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)
• Complete Characterization for Adjustment in Summary Causal Graphs of Time Series [2.1711205684359243]
  identifiability problem for interventions aims at assessing whether the total causal effect can be written with a do-free formula, and thus be estimated from observational data only.<n>We study this problem, considering multiple interventions, in the context of time series when only an abstraction of the true causal graph, in the form of a summary causal graph, is available.<n>We propose in particular both necessary and sufficient conditions for the adjustment criterion, which we show is complete in this setting, and provide a pseudo-linear algorithm to decide whether the query is identifiable or not.
  arXiv  Detail & Related papers  (2025-06-17T14:00:31Z)
• 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)
• Identifiability of total effects from abstractions of time series causal graphs [2.5515299924109858]
  We study the problem of identifiability of the total effect of an intervention from observational time series.<n>We consider two abstractions: the extended summary causal graph and the summary causal graph.
  arXiv  Detail & Related papers  (2023-10-23T08:31:26Z)
• Nonparametric Identifiability of Causal Representations from Unknown Interventions [63.1354734978244]
  We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables. Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
  arXiv  Detail & Related papers  (2023-06-01T10:51:58Z)
• Causal discovery for time series with constraint-based model and PMIME measure [0.0]
  We present a novel approach for discovering causality in time series data that combines a causal discovery algorithm with an information theoretic-based measure. We evaluate the performance of our approach on several simulated data sets, showing promising results.
  arXiv  Detail & Related papers  (2023-05-31T09:38:50Z)
• Inferring extended summary causal graphs from observational time series [4.263043028086137]
  We make use of information-theoretic measures to determine (in)dependencies between time series. The behavior of our methods is illustrated through several experiments run on simulated and real datasets.
  arXiv  Detail & Related papers  (2022-05-19T09:39:57Z)
• 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)
• Entropy-based Discovery of Summary Causal Graphs in Time Series [3.360922672565234]
  We first propose a new causal temporal mutual information measure for time series. We then show how this measure relates to an entropy reduction principle. We combine these two ingredients in PC-like and FCI-like algorithms to construct the summary causal graph.
  arXiv  Detail & Related papers  (2021-05-21T14:47:18Z)
• 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)
• Disentangling Observed Causal Effects from Latent Confounders using Method of Moments [67.27068846108047]
  We provide guarantees on identifiability and learnability under mild assumptions. We develop efficient algorithms based on coupled tensor decomposition with linear constraints to obtain scalable and guaranteed solutions.
  arXiv  Detail & Related papers  (2021-01-17T07:48:45Z)

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.