Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference
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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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 27 of its 64 references that are in the map. First posted on arXiv 20 Oct 2022.
Builds on
27 of 64 references.
References · 27
- 2021Causal Matrix Completioncited by 45
- 2016Identification and estimation of treatment and interference effects in observational studies on networkscited by 43
- 2013Graph cluster randomization: network exposure to multiple universescited by 42
- 2014Design and analysis of experiments in networks: Reducing bias from interferencecited by 41
- 2018Synthetic Difference in Differencescited by 41
- 2020Experimental Design in Two-Sided Platforms: An Analysis of Biascited by 39
- 2020Causal Inference under Networked Interference and Intervention Policy Enhancementcited by 37
- 2017Causal inference for social network datacited by 30
- 2019On Robustness of Principal Component Regressioncited by 27
- 2019Causal Inference Under Interference And Network Uncertaintycited by 27
- 2020Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimationcited by 25
- 2021Multiple Randomization Designs: Estimation and Inference with Interferencecited by 20
15 more
- 2022Estimating Total Treatment Effect in Randomized Experiments with Unknown Network Structurecited by 18
- 2017Elements of estimation theory for causal effects in the presence of network interferencecited by 12
- 2020Heterogeneous Treatment and Spillover Effects under Clustered Network Interferencecited by 11
- 2013Estimating Average Causal Effects Under General Interference, with Application to a Social Network Experimentcited by 10
- 2018Regression adjustments for estimating the global treatment effect in experiments with interferencecited by 10
- 2015Model-assisted design of experiments in the presence of network correlated outcomescited by 9
- 2020Experimental Design under Network Interferencecited by 9
- 2017Testing for arbitrary interference on experimentation platformscited by 8
- 2022Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledgecited by 8
- 2022Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Designcited by 7
- 2017Limitations of design-based causal inference and A/B testing under arbitrary and network interferencecited by 6
- 2021The Local Approach to Causal Inference under Network Interferencecited by 6
- 2018A systematic investigation of classical causal inference strategies under mis-specification due to network interferencecited by 4
- 2021Causal Inference under Network Interference with Noisecited by 4
- 2020Identifying Causal Effects in Experiments with Spillovers and Non-compliancecited by 2
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.