Experimentation under Treatment Dependent Network Interference

Shiv Shankar, Ritwik Sinha, Madalina Fiterau
Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence, PMLR 286:3787-3808, 2025.

Abstract

Randomized Controlled Trials (RCTs) are a fundamental aspect of data-driven decision-making. RCTs often assume that the units are not influenced by each other. Traditional approaches addressing such effects assume a fixed network structure between the interfering units. However, real-world networks are rarely static, and treatment assignments can actively reshape the interference structure itself, as seen in financial access interventions that alter informal lending networks or healthcare programs that modify peer influence dynamics. This creates a novel and unexplored problem: estimating treatment effects when the interference network is determined by treatment allocation. In this work, we address this gap by proposing two single-experiment estimators for scenarios where network edges depend on nodal treatments constructed from instrumental variables derived from neighbourhood treatments. We prove their unbiasedness and experimentally validate the proposed estimators both on synthetic and real data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v286-shankar25a, title = {Experimentation under Treatment Dependent Network Interference}, author = {Shankar, Shiv and Sinha, Ritwik and Fiterau, Madalina}, booktitle = {Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence}, pages = {3787--3808}, year = {2025}, editor = {Chiappa, Silvia and Magliacane, Sara}, volume = {286}, series = {Proceedings of Machine Learning Research}, month = {21--25 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v286/main/assets/shankar25a/shankar25a.pdf}, url = {https://proceedings.mlr.press/v286/shankar25a.html}, abstract = {Randomized Controlled Trials (RCTs) are a fundamental aspect of data-driven decision-making. RCTs often assume that the units are not influenced by each other. Traditional approaches addressing such effects assume a fixed network structure between the interfering units. However, real-world networks are rarely static, and treatment assignments can actively reshape the interference structure itself, as seen in financial access interventions that alter informal lending networks or healthcare programs that modify peer influence dynamics. This creates a novel and unexplored problem: estimating treatment effects when the interference network is determined by treatment allocation. In this work, we address this gap by proposing two single-experiment estimators for scenarios where network edges depend on nodal treatments constructed from instrumental variables derived from neighbourhood treatments. We prove their unbiasedness and experimentally validate the proposed estimators both on synthetic and real data.} }
Endnote
%0 Conference Paper %T Experimentation under Treatment Dependent Network Interference %A Shiv Shankar %A Ritwik Sinha %A Madalina Fiterau %B Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2025 %E Silvia Chiappa %E Sara Magliacane %F pmlr-v286-shankar25a %I PMLR %P 3787--3808 %U https://proceedings.mlr.press/v286/shankar25a.html %V 286 %X Randomized Controlled Trials (RCTs) are a fundamental aspect of data-driven decision-making. RCTs often assume that the units are not influenced by each other. Traditional approaches addressing such effects assume a fixed network structure between the interfering units. However, real-world networks are rarely static, and treatment assignments can actively reshape the interference structure itself, as seen in financial access interventions that alter informal lending networks or healthcare programs that modify peer influence dynamics. This creates a novel and unexplored problem: estimating treatment effects when the interference network is determined by treatment allocation. In this work, we address this gap by proposing two single-experiment estimators for scenarios where network edges depend on nodal treatments constructed from instrumental variables derived from neighbourhood treatments. We prove their unbiasedness and experimentally validate the proposed estimators both on synthetic and real data.
APA
Shankar, S., Sinha, R. & Fiterau, M.. (2025). Experimentation under Treatment Dependent Network Interference. Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 286:3787-3808 Available from https://proceedings.mlr.press/v286/shankar25a.html.

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