# Estimating Causal Effects in Networks with Cluster-Based Bandits

arXiv: https://arxiv.org/abs/2505.04200 · HTML: https://mlsanity.com/paper/2505.04200/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 7 May 2025.

## Builds on (most cited first, 6 of 6)

- [Causal Bandits: Learning Good Interventions via Causal Inference](https://mlsanity.com/paper/1606.03203/) (2016)
- [Graph cluster randomization: network exposure to multiple universes](https://mlsanity.com/paper/1305.6979/) (2013)
- [Cooperative Multi-Agent Bandits with Heavy Tails](https://mlsanity.com/paper/2008.06244/) (2020)
- [Local Clustering in Contextual Multi-Armed Bandits](https://mlsanity.com/paper/2103.00063/) (2021)
- [Thompson sampling with the online bootstrap](https://mlsanity.com/paper/1410.4009/) (2014)
- [Minimizing Interference and Selection Bias in Network Experiment Design](https://mlsanity.com/paper/2004.07225/) (2020)

## Abstract (arXiv)

The gold standard for estimating causal effects is randomized controlled trial (RCT) or A/B testing where a random group of individuals from a population of interest are given treatment and the outcome is compared to a random group of individuals from the same population. However, A/B testing is challenging in the presence of interference, commonly occurring in social networks, where individuals can impact each others outcome. Moreover, A/B testing can incur a high performance loss when one of the treatment arms has a poor performance and the test continues to treat individuals with it. Therefore, it is important to design a strategy that can adapt over time and efficiently learn the total treatment effect in the network. We introduce two cluster-based multi-armed bandit (MAB) algorithms to gradually estimate the total treatment effect in a network while maximizing the expected reward by making a tradeoff between exploration and exploitation. We compare the performance of our MAB algorithms with a vanilla MAB algorithm that ignores clusters and the corresponding RCT methods on semi-synthetic data with simulated interference. The vanilla MAB algorithm shows higher reward-action ratio at the cost of higher treatment effect error due to undesired spillover. The cluster-based MAB algorithms show higher reward-action ratio compared to their corresponding RCT methods without sacrificing much accuracy in treatment effect estimation.