# Bayesian Estimation of Causal Effects Using Proxies of a Latent Interference Network

arXiv: https://arxiv.org/abs/2505.08395 · HTML: https://mlsanity.com/paper/2505.08395/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 64 references that are in the map.
- First posted on arXiv 13 May 2025.

## Builds on (most cited first, 28 of 28)

- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro](https://mlsanity.com/paper/1912.11554/) (2019)
- [Oops I Took A Gradient: Scalable Sampling for Discrete Distributions](https://mlsanity.com/paper/2102.04509/) (2021)
- [Multilayer Networks](https://mlsanity.com/paper/1309.7233/) (2013)
- [Bayesian Workflow](https://mlsanity.com/paper/2011.01808/) (2020)
- [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)
- [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)
- [Bayesian Causal Inference: A Critical Review](https://mlsanity.com/paper/2206.15460/) (2022)
- [Estimating Total Treatment Effect in Randomized Experiments with Unknown Network Structure](https://mlsanity.com/paper/2205.12803/) (2022)
- [Auto-G-Computation of Causal Effects on a Network](https://mlsanity.com/paper/1709.01577/) (2017)
- [Causal inference with misspecified exposure mappings: separating definitions and assumptions](https://mlsanity.com/paper/2103.06471/) (2021)
- [Combinatorial Analysis of Multiple Networks](https://mlsanity.com/paper/1303.4986/) (2013)
- [A Graph-Theoretic Approach to Randomization Tests of Causal Effects Under General Interference](https://mlsanity.com/paper/1910.10862/) (2019)
- [Causal Message Passing for Experiments with Unknown and General Network Interference](https://mlsanity.com/paper/2311.08340/) (2023)
- [Better together? Statistical learning in models made of modules](https://mlsanity.com/paper/1708.08719/) (2017)
- [Causal Inference under Network Interference with Noise](https://mlsanity.com/paper/2105.04518/) (2021)
- [Degree of Interference: A General Framework For Causal Inference Under Interference](https://mlsanity.com/paper/2210.17516/) (2022)
- [Causal inference with misspecified network interference structure](https://mlsanity.com/paper/2302.11322/) (2023)
- [Causal health impacts of power plant emission controls under modeled and uncertain physical process interference](https://mlsanity.com/paper/2306.05665/) (2023)
- [Estimating spillovers using imprecisely measured networks](https://mlsanity.com/paper/1904.00136/) (2019)
- [A Latent Space Model for Multilayer Network Data](https://mlsanity.com/paper/2102.09560/) (2021)
- [Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data](https://mlsanity.com/paper/2112.11396/) (2021)
- [Estimating network-mediated causal effects via principal components network regression](https://mlsanity.com/paper/2212.12041/) (2022)
- [Estimation and inference for causal spillover effects in egocentric-network randomized trials in the presence of network membership misclassification](https://mlsanity.com/paper/2310.02151/) (2023)
- [Model-Based Inference and Experimental Design for Interference Using Partial Network Data](https://mlsanity.com/paper/2406.11940/) (2024)

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

- [Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Framework](https://mlsanity.com/paper/2602.03459/) (2026)

## Abstract (arXiv)

Network interference occurs when treatments assigned to some units affect the outcomes of others. Traditional approaches often assume that the observed network correctly specifies the interference structure. However, in practice, researchers frequently only have access to proxy measurements of the interference network due to limitations in data collection or potential mismatches between measured networks and actual interference pathways. In this paper, we introduce a framework for estimating causal effects when only proxy networks are available. Our approach leverages a structural causal model that accommodates diverse proxy types, including noisy measurements, multiple data sources, and multilayer networks, and defines causal effects as interventions on population-level treatments. The latent nature of the true interference network poses significant challenges. To overcome them, we develop a Bayesian inference framework. We propose a Block Gibbs sampler with Locally Informed Proposals to update the latent network, thereby efficiently exploring the high-dimensional posterior space composed of both discrete and continuous parameters. The latent network updates are driven by information from the proxy networks, treatments, and outcomes. We illustrate the performance of our method through numerical experiments, demonstrating its accuracy in recovering causal effects even when only proxies of the interference network are available.