# Causal Inference for Influence Propagation -- Identifiability of the Independent Cascade Model

arXiv: https://arxiv.org/abs/2107.04224 · HTML: https://mlsanity.com/paper/2107.04224/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2021.

## Built on by (most cited first, 2 of 2)

- [Combinatorial Causal Bandits without Graph Skeleton](https://mlsanity.com/paper/2301.13392/) (2023)
- [Contextual Procurement Auctions with Bandit Learning](https://mlsanity.com/paper/2607.05813/) (2026)

## Abstract (arXiv)

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.