# Elements of estimation theory for causal effects in the presence of network interference

arXiv: https://arxiv.org/abs/1702.03578 · HTML: https://mlsanity.com/paper/1702.03578/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2017.

## Built on by (most cited first, 12 of 12)

- [Neighborhood Adaptive Estimators for Causal Inference under Network Interference](https://mlsanity.com/paper/2212.03683/) (2022)
- [Generative AI collective behavior needs an interactionist paradigm](https://mlsanity.com/paper/2601.10567/) (2026)
- [Higher-Order Causal Message Passing for Experimentation with Complex Interference](https://mlsanity.com/paper/2411.00945/) (2024)
- [A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing](https://mlsanity.com/paper/2308.09790/) (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)
- [Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference](https://mlsanity.com/paper/2210.11355/) (2022)
- [Causal Influences over Social Learning Networks](https://mlsanity.com/paper/2307.09575/) (2023)
- [A/B testing under Interference with Partial Network Information](https://mlsanity.com/paper/2404.10547/) (2024)
- [HNCI: High-Dimensional Network Causal Inference](https://mlsanity.com/paper/2412.18568/) (2024)
- [Adaptive Policy Learning Under Unknown Network Interference](https://mlsanity.com/paper/2605.11191/) (2026)
- [Learning to target with network interference](https://mlsanity.com/paper/2605.27794/) (2026)

## Abstract (arXiv)

Randomized experiments in which the treatment of a unit can affect the outcomes of other units are becoming increasingly common in healthcare, economics, and in the social and information sciences. From a causal inference perspective, the typical assumption of no interference becomes untenable in such experiments. In many problems, however, the patterns of interference may be informed by the observation of network connections among the units of analysis. Here, we develop elements of optimal estimation theory for causal effects leveraging an observed network, by assuming that the potential outcomes of an individual depend only on the individual's treatment and on the treatment of the neighbors. We propose a collection of exclusion restrictions on the potential outcomes, and show how subsets of these restrictions lead to various parameterizations. Considering the class of linear unbiased estimators of the average direct treatment effect, we derive conditions on the design that lead to the existence of unbiased estimators, and offer analytical insights on the weights that lead to minimum integrated variance estimators. We illustrate the improved performance of these estimators when compared to more standard biased and unbiased estimators, using simulations.