Dhanya Sridhar


Assistant Professor


Curriculum vitae


dhanya.sridhar <at> mila.quebec


DIRO


University of Montreal, Mila


F.04, 6666 Rue St. Urbain



Estimating Social Influence from Observational Data


Journal article


Dhanya Sridhar, C. Bacco, D. Blei
CLeaR, 2022

Semantic Scholar ArXiv DBLP DOI
Cite

Cite

APA
Sridhar, D., Bacco, C., & Blei, D. (2022). Estimating Social Influence from Observational Data. CLeaR.

Chicago/Turabian
Sridhar, Dhanya, C. Bacco, and D. Blei. “Estimating Social Influence from Observational Data.” CLeaR (2022).

MLA
Sridhar, Dhanya, et al. “Estimating Social Influence from Observational Data.” CLeaR, 2022.


Abstract

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 contribu-tions. 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.