# Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference

arXiv: https://arxiv.org/abs/2210.11355 · HTML: https://mlsanity.com/paper/2210.11355/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 64 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Builds on (most cited first, 27 of 27)

- [Causal Matrix Completion](https://mlsanity.com/paper/2109.15154/) (2021)
- [Identification and estimation of treatment and interference effects in observational studies on networks](https://mlsanity.com/paper/1609.06245/) (2016)
- [Graph cluster randomization: network exposure to multiple universes](https://mlsanity.com/paper/1305.6979/) (2013)
- [Design and analysis of experiments in networks: Reducing bias from interference](https://mlsanity.com/paper/1404.7530/) (2014)
- [Synthetic Difference in Differences](https://mlsanity.com/paper/1812.09970/) (2018)
- [Experimental Design in Two-Sided Platforms: An Analysis of Bias](https://mlsanity.com/paper/2002.05670/) (2020)
- [Causal Inference under Networked Interference and Intervention Policy Enhancement](https://mlsanity.com/paper/2002.08506/) (2020)
- [Causal inference for social network data](https://mlsanity.com/paper/1705.08527/) (2017)
- [On Robustness of Principal Component Regression](https://mlsanity.com/paper/1902.10920/) (2019)
- [Causal Inference Under Interference And Network Uncertainty](https://mlsanity.com/paper/1907.00221/) (2019)
- [Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation](https://mlsanity.com/paper/2006.06135/) (2020)
- [Multiple Randomization Designs: Estimation and Inference with Interference](https://mlsanity.com/paper/2112.13495/) (2021)
- [Estimating Total Treatment Effect in Randomized Experiments with Unknown Network Structure](https://mlsanity.com/paper/2205.12803/) (2022)
- [Elements of estimation theory for causal effects in the presence of network interference](https://mlsanity.com/paper/1702.03578/) (2017)
- [Heterogeneous Treatment and Spillover Effects under Clustered Network Interference](https://mlsanity.com/paper/2008.00707/) (2020)
- [Estimating Average Causal Effects Under General Interference, with Application to a Social Network Experiment](https://mlsanity.com/paper/1305.6156/) (2013)
- [Regression adjustments for estimating the global treatment effect in experiments with interference](https://mlsanity.com/paper/1808.08683/) (2018)
- [Model-assisted design of experiments in the presence of network correlated outcomes](https://mlsanity.com/paper/1507.00803/) (2015)
- [Experimental Design under Network Interference](https://mlsanity.com/paper/2003.08421/) (2020)
- [Testing for arbitrary interference on experimentation platforms](https://mlsanity.com/paper/1704.01190/) (2017)
- [Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledge](https://mlsanity.com/paper/2205.14552/) (2022)
- [Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Design](https://mlsanity.com/paper/2208.05553/) (2022)
- [Limitations of design-based causal inference and A/B testing under arbitrary and network interference](https://mlsanity.com/paper/1705.05752/) (2017)
- [The Local Approach to Causal Inference under Network Interference](https://mlsanity.com/paper/2105.03810/) (2021)
- [A systematic investigation of classical causal inference strategies under mis-specification due to network interference](https://mlsanity.com/paper/1810.08259/) (2018)
- [Causal Inference under Network Interference with Noise](https://mlsanity.com/paper/2105.04518/) (2021)
- [Identifying Causal Effects in Experiments with Spillovers and Non-compliance](https://mlsanity.com/paper/2011.07051/) (2020)

## Built on by (most cited first, 2 of 2)

- [Multi-Armed Bandits with Network Interference](https://mlsanity.com/paper/2405.18621/) (2024)
- [Causal Inference for Sequential Settings under Interference and Latent Confounding](https://mlsanity.com/paper/2607.14940/) (2026)

## Abstract (arXiv)

We propose a generalization of the synthetic controls and synthetic interventions methodology to incorporate network interference. We consider the estimation of unit-specific potential outcomes from panel data in the presence of spillover across units and unobserved confounding. Key to our approach is a novel latent factor model that takes into account network interference and generalizes the factor models typically used in panel data settings. We propose an estimator, Network Synthetic Interventions (NSI), and show that it consistently estimates the mean outcomes for a unit under an arbitrary set of counterfactual treatments for the network. We further establish that the estimator is asymptotically normal. We furnish two validity tests for whether the NSI estimator reliably generalizes to produce accurate counterfactual estimates. We provide a novel graph-based experiment design that guarantees the NSI estimator produces accurate counterfactual estimates, and also analyze the sample complexity of the proposed design. We conclude with simulations that corroborate our theoretical findings.