Single-Network Asymptotics for Causal Inference with Partial Network Data

Steven Wilkins-Reeves, Shane Lubold, Arun Chandrasekhar, Tyler McCormick
Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence, PMLR 337:7341-7369, 2026.

Abstract

Randomized experiments are a staple in academic research, policy, and industry. Interference, when the outcome of one unit depends on the treatment status of other units, can cause bias in estimates of the treatment effect. Statistical corrections rely on knowing the underlying network of transmission pathways. Often, though, it is only possible to partially observe the network (e.g., through node subsamples, egocentric designs, respondent-driven sampling, or aggregated relational data). We develop a single-network asymptotic framework for inference about treatment effects in this regime. Starting from a structural causal model and exposure mapping, we assume a flexible class of generative network models consistent with the partial measurements and construct feasible proxy exposures by averaging over plausible completions of the network. We then study moment-based estimators for response parameters and treatment effects under dependence, and give conditions for consistency and asymptotic normality together with an explicit rate requirement ensuring that network-estimation error is first-order negligible. Simulations and an empirical replication illustrate how the theory maps to finite-sample workflows for interference with partial network data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v337-wilkins-reeves26a, title = {Single-Network Asymptotics for Causal Inference with Partial Network Data}, author = {Wilkins-Reeves, Steven and Lubold, Shane and Chandrasekhar, Arun and McCormick, Tyler}, booktitle = {Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence}, pages = {7341--7369}, year = {2026}, editor = {Perković, Emilija and Malinsky, Daniel}, volume = {337}, series = {Proceedings of Machine Learning Research}, month = {17--21 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v337/main/assets/wilkins-reeves26a/wilkins-reeves26a.pdf}, url = {https://proceedings.mlr.press/v337/wilkins-reeves26a.html}, abstract = {Randomized experiments are a staple in academic research, policy, and industry. Interference, when the outcome of one unit depends on the treatment status of other units, can cause bias in estimates of the treatment effect. Statistical corrections rely on knowing the underlying network of transmission pathways. Often, though, it is only possible to partially observe the network (e.g., through node subsamples, egocentric designs, respondent-driven sampling, or aggregated relational data). We develop a single-network asymptotic framework for inference about treatment effects in this regime. Starting from a structural causal model and exposure mapping, we assume a flexible class of generative network models consistent with the partial measurements and construct feasible proxy exposures by averaging over plausible completions of the network. We then study moment-based estimators for response parameters and treatment effects under dependence, and give conditions for consistency and asymptotic normality together with an explicit rate requirement ensuring that network-estimation error is first-order negligible. Simulations and an empirical replication illustrate how the theory maps to finite-sample workflows for interference with partial network data.} }
Endnote
%0 Conference Paper %T Single-Network Asymptotics for Causal Inference with Partial Network Data %A Steven Wilkins-Reeves %A Shane Lubold %A Arun Chandrasekhar %A Tyler McCormick %B Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2026 %E Emilija Perković %E Daniel Malinsky %F pmlr-v337-wilkins-reeves26a %I PMLR %P 7341--7369 %U https://proceedings.mlr.press/v337/wilkins-reeves26a.html %V 337 %X Randomized experiments are a staple in academic research, policy, and industry. Interference, when the outcome of one unit depends on the treatment status of other units, can cause bias in estimates of the treatment effect. Statistical corrections rely on knowing the underlying network of transmission pathways. Often, though, it is only possible to partially observe the network (e.g., through node subsamples, egocentric designs, respondent-driven sampling, or aggregated relational data). We develop a single-network asymptotic framework for inference about treatment effects in this regime. Starting from a structural causal model and exposure mapping, we assume a flexible class of generative network models consistent with the partial measurements and construct feasible proxy exposures by averaging over plausible completions of the network. We then study moment-based estimators for response parameters and treatment effects under dependence, and give conditions for consistency and asymptotic normality together with an explicit rate requirement ensuring that network-estimation error is first-order negligible. Simulations and an empirical replication illustrate how the theory maps to finite-sample workflows for interference with partial network data.
APA
Wilkins-Reeves, S., Lubold, S., Chandrasekhar, A. & McCormick, T.. (2026). Single-Network Asymptotics for Causal Inference with Partial Network Data. Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 337:7341-7369 Available from https://proceedings.mlr.press/v337/wilkins-reeves26a.html.

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