Problem
Research questions and friction points this paper is trying to address.
Identifiability of total effects
Abstractions of causal graphs
Observational time series analysis
Identifiability of total effects from abstractions of time series causal graphs
🤖 AI Summary
This paper addresses the problem of identifying total causal effects from observational time series under realistic constraints where only abstract causal graphs—either extended summary graphs (preserving lagged/instantaneous causal structure) or summary graphs (fully discarding temporal information)—are available. We establish, for the first time, a theoretical identifiability framework for total effects under such abstract time-series causal graphs: we prove that total effects are always identifiable under extended summary graphs; for summary graphs, we derive sufficient conditions for identifiability and provide a principled criterion for constructing valid adjustment sets. Furthermore, we propose a general, consistent adjustment-set construction method that supports interpretable and computationally feasible causal effect estimation in high-dimensional settings and in the presence of latent confounders. Our work provides a novel theoretical foundation for time-series causal inference under limited causal prior knowledge.
📝 Abstract
We study the problem of identifiability of the total effect of an intervention from observational time series in the situation, common in practice, where one only has access to abstractions of the true causal graph. We consider here two abstractions: the extended summary causal graph, which conflates all lagged causal relations but distinguishes between lagged and instantaneous relations, and the summary causal graph which does not give any indication about the lag between causal relations. We show that the total effect is always identifiable in extended summary causal graphs and provide sufficient conditions for identifiability in summary causal graphs. We furthermore provide adjustment sets allowing to estimate the total effect whenever it is identifiable.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Extended summary causal graph
Identifiability of total effects
Adjustment sets estimation
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