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We propose a matching method that recovers direct treatment effects fromrandomized experiments where units are connected in an observed network, andunits that share edges can potentially influence each others' outcomes.Traditional treatment effect estimators for randomized experiments are biasedand error prone in this setting. Our method matches units almost exactly oncounts of unique subgraphs within their neighborhood graphs. The matches thatwe construct are interpretable and high-quality. Our method can be extendedeasily to accommodate additional unit-level covariate information. We showempirically that our method performs better than other existing methodologiesfor this problem, while producing meaningful, interpretable results.

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