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

03/02/2020
by   M. Usaid Awan, et al.
0

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.

READ FULL TEXT

page 32

page 35

page 37

research
03/03/2020

Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation

We propose a matching method for observational data that matches units w...
research
11/17/2017

Average treatment effects in the presence of unknown interference

We investigate large-sample properties of treatment effect estimators un...
research
07/26/2021

Efficient Treatment Effect Estimation in Observational Studies under Heterogeneous Partial Interference

In many observational studies in social science and medical applications...
research
06/28/2018

Using Exposure Mappings as Side Information in Experiments with Interference

Exposure mappings are widely used to model potential outcomes in the pre...
research
01/06/2021

dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference

dame-flame is a Python package for performing matching for observational...
research
06/18/2018

Collapsing-Fast-Large-Almost-Matching-Exactly: A Matching Method for Causal Inference

We aim to create the highest possible quality of treatment-control match...
research
11/18/2018

MALTS: Matching After Learning to Stretch

We introduce a flexible framework for matching in causal inference that ...

Please sign up or login with your details

Forgot password? Click here to reset