# Model-Based Inference and Experimental Design for Interference Using Partial Network Data

arXiv: https://arxiv.org/abs/2406.11940 · HTML: https://mlsanity.com/paper/2406.11940/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 106 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Graph cluster randomization: network exposure to multiple universes](https://mlsanity.com/paper/1305.6979/) (2013)
- [Experimental Design under Network Interference](https://mlsanity.com/paper/2003.08421/) (2020)
- [Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledge](https://mlsanity.com/paper/2205.14552/) (2022)
- [Neighborhood Adaptive Estimators for Causal Inference under Network Interference](https://mlsanity.com/paper/2212.03683/) (2022)
- [The Local Approach to Causal Inference under Network Interference](https://mlsanity.com/paper/2105.03810/) (2021)
- [Cluster Randomized Designs for One-Sided Bipartite Experiments](https://mlsanity.com/paper/2210.16415/) (2022)
- [Multiply Robust Estimation for Local Distribution Shifts with Multiple Domains](https://mlsanity.com/paper/2402.14145/) (2024)
- [Estimating spillovers using imprecisely measured networks](https://mlsanity.com/paper/1904.00136/) (2019)
- [General Covariance-Based Conditions for Central Limit Theorems with Dependent Triangular Arrays](https://mlsanity.com/paper/2308.12506/) (2023)
- [Estimation of dense stochastic block models visited by random walks](https://mlsanity.com/paper/2006.08010/) (2020)
- [Causal Effects with Hidden Treatment Diffusion on Observed or Partially Observed Networks](https://mlsanity.com/paper/2109.07502/) (2021)
- [Asymptotically Normal Estimation of Local Latent Network Curvature](https://mlsanity.com/paper/2211.11673/) (2022)

## Built on by (most cited first, 1 of 1)

- [Bayesian Estimation of Causal Effects Using Proxies of a Latent Interference Network](https://mlsanity.com/paper/2505.08395/) (2025)

## Abstract (arXiv)

The stable unit treatment value assumption states that the outcome of an individual is not affected by the treatment statuses of others, however in many real world applications, treatments can have an effect on many others beyond the immediately treated. Interference can generically be thought of as mediated through some network structure. In many empirically relevant situations however, complete network data (required to adjust for these spillover effects) are too costly or logistically infeasible to collect. Partially or indirectly observed network data (e.g., subsamples, aggregated relational data (ARD), egocentric sampling, or respondent-driven sampling) reduce the logistical and financial burden of collecting network data, but the statistical properties of treatment effect adjustments from these design strategies are only beginning to be explored. In this paper, we present a framework for the estimation and inference of treatment effect adjustments using partial network data through the lens of structural causal models. We also illustrate procedures to assign treatments using only partial network data, with the goal of either minimizing estimator variance or optimally seeding. We derive single network asymptotic results applicable to a variety of choices for an underlying graph model. We validate our approach using simulated experiments on observed graphs with applications to information diffusion in India and Malawi.