Complete Characterization for Adjustment in Summary Causal Graphs of Time Series

Clément Yvernes, Emilie Devijver, Eric Gaussier
Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence, PMLR 286:4844-4871, 2025.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v286-yvernes25a, title = {Complete Characterization for Adjustment in Summary Causal Graphs of Time Series}, author = {Yvernes, Cl\'{e}ment and Devijver, Emilie and Gaussier, Eric}, booktitle = {Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence}, pages = {4844--4871}, year = {2025}, editor = {Chiappa, Silvia and Magliacane, Sara}, volume = {286}, series = {Proceedings of Machine Learning Research}, month = {21--25 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v286/main/assets/yvernes25a/yvernes25a.pdf}, url = {https://proceedings.mlr.press/v286/yvernes25a.html}, 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.} }
Endnote
%0 Conference Paper %T Complete Characterization for Adjustment in Summary Causal Graphs of Time Series %A Clément Yvernes %A Emilie Devijver %A Eric Gaussier %B Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2025 %E Silvia Chiappa %E Sara Magliacane %F pmlr-v286-yvernes25a %I PMLR %P 4844--4871 %U https://proceedings.mlr.press/v286/yvernes25a.html %V 286 %X 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.
APA
Yvernes, C., Devijver, E. & Gaussier, E.. (2025). Complete Characterization for Adjustment in Summary Causal Graphs of Time Series. Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 286:4844-4871 Available from https://proceedings.mlr.press/v286/yvernes25a.html.

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