mlsanityData to 14 Oct 2026

Estimating Social Influence from Observational Data

arXiv 2204.01633 ↗First posted 24 Mar 2022cs.SI cs.AI cs.LGcited by 5

Not on the map. Why?

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.

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

20 of 42 references.

References · 20

  1. 2016Variational Inference: A Review for Statisticianscited by 1,472
  2. 2018The Blessings of Multiple Causescited by 104
  3. 2019Learning Individual Causal Effects from Networked Observational Datacited by 56
  4. 2017Causal inference for social network datacited by 31
  5. 2019Using Embeddings to Correct for Unobserved Confounding in Networkscited by 31
  6. 2017Bayesian stochastic blockmodelingcited by 27
  7. 2017Community detection, link prediction, and layer interdependence in multilayer networkscited by 17
  8. 2019Comment on "Blessings of Multiple Causes"cited by 16
  9. 2020Community detection with node attributes in multilayer networkscited by 14
  10. 2020Towards Clarifying the Theory of the Deconfoundercited by 9
  11. 2013Scalable Recommendation with Poisson Factorizationcited by 8
  12. 2016Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locationscited by 8
8 more
  1. 2019Identification and Estimation of Causal Effects from Dependent Datacited by 8
  2. 2020Counterexamples to "The Blessings of Multiple Causes" by Wang and Bleicited by 8
  3. 2017Bias and high-dimensional adjustment in observational studies of peer effectscited by 7
  4. 2018Detecting Social Influence in Event Cascades by Comparing Discriminative Rankerscited by 3
  5. 2013Selection Effects in Online Sharing: Consequences for Peer Adoptioncited by 2
  6. 2016Distinguishing between Personal Preferences and Social Influence in Online Activity Feedscited by 2
  7. 2020General Identification of Dynamic Treatment Regimes Under Interferencecited by 2
  8. 2016Split-door criterion: Identification of causal effects through auxiliary outcomescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 1; 2026: 1.

5 papers.

  1. 2026EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environmentscited by 20
  2. 2022Using Embeddings for Causal Estimation of Peer Influence in Social Networkscited by 9
  3. 2022Identifying Peer Influence in Therapeutic Communities Adjusting for Latent Homophilycited by 1
  4. 2023Causal Influences over Social Learning Networkscited by 1
  5. 2025Detoxify: A framework for abusive text transformation using LLMscited by 0