Complete Characterization for Adjustment in Summary Causal Graphs of Time Series
This addresses the identifiability problem for interventions in time series analysis, providing theoretical completeness for researchers in causal inference.
The paper tackles the problem of determining when total causal effects can be estimated from observational data in time series with summary causal graphs, proposing necessary and sufficient conditions for the adjustment criterion and showing it is complete in this setting.
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.