Complete Characterization for Adjustment in Summary Causal Graphs of Time Series

2025-06-17

AI Summary

This paper addresses the identifiability of total causal effects under multiple interventions in time-series settings where only a summary causal graph is known—specifically, whether such effects admit a do-free observational equivalent expression. We establish the first necessary and sufficient condition for adjustment-based identifiability under summary causal graphs and prove its theoretical completeness. Based on this, we design a pseudo-linear-time algorithm (nearly O(|E|)) for identifiability determination, substantially outperforming existing approaches. The algorithm uniformly handles multiple interventions, temporal dependencies, and sparse graph structures, enabling efficient causal effect identification in high-dimensional settings. Our main contributions are: (i) the first theoretical framework for adjustment criteria tailored to time-series summary graphs; (ii) a decidable, computationally tractable, and scalable do-free identification procedure; and (iii) rigorous guarantees of soundness and completeness for both the criterion and the algorithm.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Assess total causal effect identifiability from observational data
• Study multiple interventions in time series with summary graphs
• Provide necessary and sufficient conditions for adjustment criterion

Innovation

• Necessary and sufficient adjustment conditions
• Complete criterion for summary graphs
• Pseudo-linear algorithm for identifiability