  Independent cascade (IC) model is a widely used influence propagation model
for social networks. In this paper, we incorporate the concept and techniques
from causal inference to study the identifiability of parameters from
observational data in extended IC model with unobserved confounding factors,
which models more realistic propagation scenarios but is rarely studied in
influence propagation modeling before. We provide the conditions for the
identifiability or unidentifiability of parameters for several special
structures including the Markovian IC model, semi-Markovian IC model, and IC
model with a global unobserved variable. Parameter identifiability is important
for other tasks such as influence maximization under the diffusion networks
with unobserved confounding factors.
