# Design and analysis of experiments in networks: Reducing bias from interference

arXiv: https://arxiv.org/abs/1404.7530 · HTML: https://mlsanity.com/paper/1404.7530/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2014.

## Built on by (most cited first, 30 of 42)

- [Size-Invariant Graph Representations for Graph Classification Extrapolations](https://mlsanity.com/paper/2103.05045/) (2021)
- [Markovian Interference in Experiments](https://mlsanity.com/paper/2206.02371/) (2022)
- [Multi-Armed Bandits with Interference](https://mlsanity.com/paper/2402.01845/) (2024)
- [Neighborhood Adaptive Estimators for Causal Inference under Network Interference](https://mlsanity.com/paper/2212.03683/) (2022)
- [Causal Message Passing for Experiments with Unknown and General Network Interference](https://mlsanity.com/paper/2311.08340/) (2023)
- [Reranking partisan animosity in algorithmic social media feeds alters affective polarization](https://mlsanity.com/paper/2411.14652/) (2024)
- [Causal Lifting and Link Prediction](https://mlsanity.com/paper/2302.01198/) (2023)
- [Graph-Dependent Regret Bounds in Multi-Armed Bandits with Interference](https://mlsanity.com/paper/2503.07555/) (2025)
- [Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects](https://mlsanity.com/paper/2403.11332/) (2024)
- [Higher-Order Causal Message Passing for Experimentation with Complex Interference](https://mlsanity.com/paper/2411.00945/) (2024)
- [Double Machine Learning for Causal Inference under Shared-State Interference](https://mlsanity.com/paper/2504.08836/) (2025)
- [Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?](https://mlsanity.com/paper/2507.11891/) (2025)
- [A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing](https://mlsanity.com/paper/2308.09790/) (2023)
- [Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach](https://mlsanity.com/paper/2310.17496/) (2023)
- [Clustered Switchback Designs for Experimentation Under Spatio-temporal Interference](https://mlsanity.com/paper/2312.15574/) (2023)
- [Scalable Policy Maximization Under Network Interference](https://mlsanity.com/paper/2505.18118/) (2025)
- [Simulating and Experimenting with Social Media Mobilization Using LLM Agents](https://mlsanity.com/paper/2510.26494/) (2025)
- [Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference](https://mlsanity.com/paper/2210.11355/) (2022)
- [Cascade-based Randomization for Inferring Causal Effects under Diffusion Interference](https://mlsanity.com/paper/2405.12340/) (2024)
- [Treatment Effect Estimation with Observational Network Data using Machine Learning](https://mlsanity.com/paper/2206.14591/) (2022)
- [Contagion Effect Estimation Using Proximal Embeddings](https://mlsanity.com/paper/2306.02479/) (2023)
- [Differences-in-Neighbors for Network Interference in Experiments](https://mlsanity.com/paper/2503.02271/) (2025)
- [Local Interference: Removing Interference Bias in Semi-Parametric Causal Models](https://mlsanity.com/paper/2503.18756/) (2025)
- [Bayesian Estimation of Causal Effects Using Proxies of a Latent Interference Network](https://mlsanity.com/paper/2505.08395/) (2025)
- [On Evolution-Based Models for Experimentation Under Interference](https://mlsanity.com/paper/2511.21675/) (2025)
- [Choosing Online Experiment Designs under Interference in Ads, Recommendations, and Member-Experience Systems](https://mlsanity.com/paper/2605.25290/) (2026)
- [Near-Optimal Experimental Design Under the Budget Constraint in Online Platforms](https://mlsanity.com/paper/2302.05005/) (2023)
- [Causal Estimation of User Learning in Personalized Systems](https://mlsanity.com/paper/2306.00485/) (2023)
- [A/B testing under Interference with Partial Network Information](https://mlsanity.com/paper/2404.10547/) (2024)
- [Towards Measuring Sell Side Outcomes in Buy Side Marketplace Experiments using In-Experiment Bipartite Graph](https://mlsanity.com/paper/2409.04174/) (2024)

## Abstract (arXiv)

Estimating the effects of interventions in networks is complicated when the units are interacting, such that the outcomes for one unit may depend on the treatment assignment and behavior of many or all other units (i.e., there is interference). When most or all units are in a single connected component, it is impossible to directly experimentally compare outcomes under two or more global treatment assignments since the network can only be observed under a single assignment. Familiar formalism, experimental designs, and analysis methods assume the absence of these interactions, and result in biased estimators of causal effects of interest. While some assumptions can lead to unbiased estimators, these assumptions are generally unrealistic, and we focus this work on realistic assumptions. Thus, in this work, we evaluate methods for designing and analyzing randomized experiments that aim to reduce this bias and thereby reduce overall error. In design, we consider the ability to perform random assignment to treatments that is correlated in the network, such as through graph cluster randomization. In analysis, we consider incorporating information about the treatment assignment of network neighbors. We prove sufficient conditions for bias reduction through both design and analysis in the presence of potentially global interference. Through simulations of the entire process of experimentation in networks, we measure the performance of these methods under varied network structure and varied social behaviors, finding substantial bias and error reductions. These improvements are largest for networks with more clustering and data generating processes with both stronger direct effects of the treatment and stronger interactions between units.