Complete Characterization for Adjustment in Summary Causal Graphs of Time Series

• URL: http://arxiv.org/abs/2506.14534v1
• Date: Tue, 17 Jun 2025 14:00:31 GMT
• Title: Complete Characterization for Adjustment in Summary Causal Graphs of Time Series
• Authors: Clément Yvernes, Emilie Devijver, Eric Gaussier,
• Abstract summary: 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.
• Score: 2.1711205684359243
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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