# Estimating Social Influence from Observational Data

arXiv: https://arxiv.org/abs/2204.01633 · HTML: https://mlsanity.com/paper/2204.01633/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 24 Mar 2022.

## Builds on (most cited first, 20 of 20)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [The Blessings of Multiple Causes](https://mlsanity.com/paper/1805.06826/) (2018)
- [Learning Individual Causal Effects from Networked Observational Data](https://mlsanity.com/paper/1906.03485/) (2019)
- [Causal inference for social network data](https://mlsanity.com/paper/1705.08527/) (2017)
- [Using Embeddings to Correct for Unobserved Confounding in Networks](https://mlsanity.com/paper/1902.04114/) (2019)
- [Bayesian stochastic blockmodeling](https://mlsanity.com/paper/1705.10225/) (2017)
- [Community detection, link prediction, and layer interdependence in multilayer networks](https://mlsanity.com/paper/1701.01369/) (2017)
- [Comment on "Blessings of Multiple Causes"](https://mlsanity.com/paper/1910.05438/) (2019)
- [Community detection with node attributes in multilayer networks](https://mlsanity.com/paper/2004.09160/) (2020)
- [Towards Clarifying the Theory of the Deconfounder](https://mlsanity.com/paper/2003.04948/) (2020)
- [Scalable Recommendation with Poisson Factorization](https://mlsanity.com/paper/1311.1704/) (2013)
- [Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations](https://mlsanity.com/paper/1607.06565/) (2016)
- [Identification and Estimation of Causal Effects from Dependent Data](https://mlsanity.com/paper/1902.01443/) (2019)
- [Counterexamples to "The Blessings of Multiple Causes" by Wang and Blei](https://mlsanity.com/paper/2001.06555/) (2020)
- [Bias and high-dimensional adjustment in observational studies of peer effects](https://mlsanity.com/paper/1706.04692/) (2017)
- [Detecting Social Influence in Event Cascades by Comparing Discriminative Rankers](https://mlsanity.com/paper/1802.06138/) (2018)
- [Selection Effects in Online Sharing: Consequences for Peer Adoption](https://mlsanity.com/paper/1311.2878/) (2013)
- [Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds](https://mlsanity.com/paper/1604.01105/) (2016)
- [General Identification of Dynamic Treatment Regimes Under Interference](https://mlsanity.com/paper/2004.01218/) (2020)
- [Split-door criterion: Identification of causal effects through auxiliary outcomes](https://mlsanity.com/paper/1611.09414/) (2016)

## Built on by (most cited first, 5 of 5)

- [EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments](https://mlsanity.com/paper/2607.05155/) (2026)
- [Using Embeddings for Causal Estimation of Peer Influence in Social Networks](https://mlsanity.com/paper/2205.08033/) (2022)
- [Identifying Peer Influence in Therapeutic Communities Adjusting for Latent Homophily](https://mlsanity.com/paper/2203.14223/) (2022)
- [Causal Influences over Social Learning Networks](https://mlsanity.com/paper/2307.09575/) (2023)
- [Detoxify: A framework for abusive text transformation using LLMs](https://mlsanity.com/paper/2507.10177/) (2025)

## Abstract (arXiv)

We consider the problem of estimating social influence, the effect that a person's behavior has on the future behavior of their peers. The key challenge is that shared behavior between friends could be equally explained by influence or by two other confounding factors: 1) latent traits that caused people to both become friends and engage in the behavior, and 2) latent preferences for the behavior. This paper addresses the challenges of estimating social influence with three contributions. First, we formalize social influence as a causal effect, one which requires inferences about hypothetical interventions. Second, we develop Poisson Influence Factorization (PIF), a method for estimating social influence from observational data. PIF fits probabilistic factor models to networks and behavior data to infer variables that serve as substitutes for the confounding latent traits. Third, we develop assumptions under which PIF recovers estimates of social influence. We empirically study PIF with semi-synthetic and real data from Last.fm, and conduct a sensitivity analysis. We find that PIF estimates social influence most accurately compared to related methods and remains robust under some violations of its assumptions.