Identifiability of Direct Effects from Summary Causal Graphs
This work addresses a key challenge in causal inference for dynamic systems where only summary graphs are available, offering practical tools for experts in fields like epidemiology or economics.
The paper tackles the problem of identifying direct causal effects from summary causal graphs, which omit temporal information, and provides a complete characterization of identifiability along with two sound finite adjustment sets for estimation.
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.