Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference
By M. Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky. 2020
15 citations recorded by the source corpus.
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.
Subfields: Statistics and Probability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
- Cynthia Rudin
- Alexander Volfovsky
- Sudeepa Roy
- Berk Ustun
- Harsh Parikh
- Kush R. Varshney
- Nadia Burkart
- Marco F. Huber
Citations in the corpus
- FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017, methodology): “FLAME has been extended to handle network interference (Awan et al., 2020) and instrumental variable analysis (Awan et al.”