# Causal Influences over Social Learning Networks

arXiv: https://arxiv.org/abs/2307.09575 · HTML: https://mlsanity.com/paper/2307.09575/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 61 references that are in the map.
- First posted on arXiv 13 Jul 2023.

## Builds on (most cited first, 16 of 16)

- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [TimeLMs: Diachronic Language Models from Twitter](https://mlsanity.com/paper/2202.03829/) (2022)
- [Fast Convergence Rates for Distributed Non-Bayesian Learning](https://mlsanity.com/paper/1508.05161/) (2015)
- [Social Learning and Distributed Hypothesis Testing](https://mlsanity.com/paper/1410.4307/) (2014)
- [Elements of estimation theory for causal effects in the presence of network interference](https://mlsanity.com/paper/1702.03578/) (2017)
- [Social Learning over Weakly-Connected Graphs](https://mlsanity.com/paper/1609.03703/) (2016)
- [Automatic Detection of Influential Actors in Disinformation Networks](https://mlsanity.com/paper/2005.10879/) (2020)
- [Information Exchange and Learning Dynamics over Weakly-Connected Adaptive Networks](https://mlsanity.com/paper/1412.1523/) (2014)
- [Algorithms for Online Influencer Marketing](https://mlsanity.com/paper/1702.05354/) (2017)
- [Estimating Social Influence from Observational Data](https://mlsanity.com/paper/2204.01633/) (2022)
- [Bayesian Decision Making in Groups is Hard](https://mlsanity.com/paper/1705.04770/) (2017)
- [Detecting Social Influence in Event Cascades by Comparing Discriminative Rankers](https://mlsanity.com/paper/1802.06138/) (2018)
- [Influence Estimation on Social Media Networks Using Causal Inference](https://mlsanity.com/paper/1804.04109/) (2018)
- [Opinion Exchange Dynamics](https://mlsanity.com/paper/1401.4770/) (2014)
- [Discovering Influencers in Opinion Formation over Social Graphs](https://mlsanity.com/paper/2211.13292/) (2022)
- [Dynamics of Social Networks: Multi-agent Information Fusion, Anticipatory Decision Making and Polling](https://mlsanity.com/paper/2212.13323/) (2022)

## Built on by (most cited first, 1 of 1)

- [Causal Influence in Federated Edge Inference](https://mlsanity.com/paper/2405.01260/) (2024)

## Abstract (arXiv)

This paper investigates causal influences between agents linked by a social graph and interacting over time. In particular, the work examines the dynamics of social learning models and distributed decision-making protocols, and derives expressions that reveal the causal relations between pairs of agents and explain the flow of influence over the network. The results turn out to be dependent on the graph topology and the level of information that each agent has about the inference problem they are trying to solve. Using these conclusions, the paper proposes an algorithm to rank the overall influence between agents to discover highly influential agents. It also provides a method to learn the necessary model parameters from raw observational data. The results and the proposed algorithm are illustrated by considering both synthetic data and real social media data.