Identifiability by common backdoor in summary causal graphs of time series
This addresses a theoretical challenge in causal inference for time series data, but it appears incremental as it builds on existing identifiability concepts.
The paper tackles the identifiability problem for interventions in time series when only summary causal graphs are available, establishing conditions for identifiability by a common backdoor set and providing algorithms to decide it.
The identifiability problem for interventions aims at assessing whether the total effect of some given interventions can be written with a do-free formula, and thus be computed from observational data only. We study this problem, considering multiple interventions and multiple effects, in the context of time series when only abstractions of the true causal graph in the form of summary causal graphs are available. We focus in this study on identifiability by a common backdoor set, and establish, for time series with and without consistency throughout time, conditions under which such a set exists. We also provide algorithms of limited complexity to decide whether the problem is identifiable or not.