Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference

Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:3252-3262, 2020.

Abstract

We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network, and units that share edges can potentially influence each others’ outcomes. Traditional treatment effect estimators for randomized experiments are biased and error prone in this setting. Our method matches units almost exactly on counts of unique subgraphs within their neighborhood graphs. The matches that we construct are interpretable and high-quality. Our method can be extended easily to accommodate additional unit-level covariate information. We show empirically that our method performs better than other existing methodologies for this problem, while producing meaningful, interpretable results.

Cite this Paper

BibTeX
@InProceedings{pmlr-v108-awan20a, title = {Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference}, author = {Awan, Usaid and Morucci, Marco and Orlandi, Vittorio and Roy, Sudeepa and Rudin, Cynthia and Volfovsky, Alexander}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {3252--3262}, year = {2020}, editor = {Chiappa, Silvia and Calandra, Roberto}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/awan20a/awan20a.pdf}, url = {https://proceedings.mlr.press/v108/awan20a.html}, abstract = {We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network, and units that share edges can potentially influence each others’ outcomes. Traditional treatment effect estimators for randomized experiments are biased and error prone in this setting. Our method matches units almost exactly on counts of unique subgraphs within their neighborhood graphs. The matches that we construct are interpretable and high-quality. Our method can be extended easily to accommodate additional unit-level covariate information. We show empirically that our method performs better than other existing methodologies for this problem, while producing meaningful, interpretable results.} }
Endnote
%0 Conference Paper %T Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference %A Usaid Awan %A Marco Morucci %A Vittorio Orlandi %A Sudeepa Roy %A Cynthia Rudin %A Alexander Volfovsky %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-awan20a %I PMLR %P 3252--3262 %U https://proceedings.mlr.press/v108/awan20a.html %V 108 %X We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network, and units that share edges can potentially influence each others’ outcomes. Traditional treatment effect estimators for randomized experiments are biased and error prone in this setting. Our method matches units almost exactly on counts of unique subgraphs within their neighborhood graphs. The matches that we construct are interpretable and high-quality. Our method can be extended easily to accommodate additional unit-level covariate information. We show empirically that our method performs better than other existing methodologies for this problem, while producing meaningful, interpretable results.
APA
Awan, U., Morucci, M., Orlandi, V., Roy, S., Rudin, C. & Volfovsky, A.. (2020). Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:3252-3262 Available from https://proceedings.mlr.press/v108/awan20a.html.

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