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Communication Dans Un Congrès Année : 2024

Identifiability of Direct Effects from Summary Causal Graphs

Résumé

Dynamic structural causal models (SCMs) are a powerful framework for reasoning in dynamic systems about direct effects which measure how a change in one variable affects another variable while holding all other variables constant. The causal relations in a dynamic structural causal model can be qualitatively represented with an acyclic full-time causal graph. Assuming linearity and no hidden confounding and given the full-time causal graph, the direct causal effect is always identifiable. However, in many application such a graph is not available for various reasons but nevertheless experts have access to the summary causal graph of the full-time causal graph which represents causal relations between time series while omitting temporal information and allowing cycles. This paper presents a complete identifiability result which characterizes all cases for which the direct effect is graphically identifiable from a summary causal graph and gives two sound finite adjustment sets that can be used to estimate the direct effect whenever it is identifiable.
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hal-04607051 , version 1 (10-06-2024)

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Simon Ferreira, Charles Assaad. Identifiability of Direct Effects from Summary Causal Graphs. AAAI Conference on Artificial Intelligence, Feb 2024, Vancouver (BC), Canada. pp.20387-20394, ⟨10.1609/aaai.v38i18.30021⟩. ⟨hal-04607051⟩
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