# A Simple Model to Estimate Sharing Effects in Social Networks

arXiv: https://arxiv.org/abs/2409.12203 · HTML: https://mlsanity.com/paper/2409.12203/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [Multiple Randomization Designs: Estimation and Inference with Interference](https://mlsanity.com/paper/2112.13495/) (2021)
- [Delta-rm OPE: Off-Policy Estimation with Pairs of Policies](https://mlsanity.com/paper/2405.10024/) (2024)
- [Network experimentation at scale](https://mlsanity.com/paper/2012.08591/) (2020)

## Abstract (arXiv)

Randomised Controlled Trials (RCTs) are the gold standard for estimating treatment effects across many fields of science. Technology companies have adopted A/B-testing methods as a modern RCT counterpart, where end-users are randomly assigned various system variants and user behaviour is tracked continuously. The objective is then to estimate the causal effect that the treatment variant would have on certain metrics of interest to the business. When the outcomes for randomisation units -- end-users in this case -- are not statistically independent, this obfuscates identifiability of treatment effects, and harms decision-makers' observability of the system. Social networks exemplify this, as they are designed to promote inter-user interactions. This interference by design notoriously complicates measurement of, e.g., the effects of sharing. In this work, we propose a simple Markov Decision Process (MDP)-based model describing user sharing behaviour in social networks. We derive an unbiased estimator for treatment effects under this model, and demonstrate through reproducible synthetic experiments that it outperforms existing methods by a significant margin.