Complete Characterization for Adjustment in Summary Causal Graphs of Time Series

Clément Yvernes, Emilie Devijver, Eric Gaussier

Keywords: Causality, Abstraction of Time Series, Identification, Adjustment, Algorithm

TL;DR: Identifiability of interventions in time series with summary causal graphs: conditions and algorithms for common adjustment sets.

Abstract: The 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. 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. 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.

Code Link: https://gricad-gitlab.univ-grenoble-alpes.fr/yvernesc/multivariateicainscg

Readers: auai.org/UAI/2025/Conference, auai.org/UAI/2025/Conference/Area_Chairs, auai.org/UAI/2025/Conference/Reviewers, auai.org/UAI/2025/Conference/Submission467/Authors, auai.org/UAI/2025/Conference/Submission467/Reproducibility_Reviewers

Submission Number: 467