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Causal Inference under Network Interference: Network Embedding Matching
Zhang, Xu
Zhang, Xu
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2023
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http://dx.doi.org/10.34944/dspace/8479
Abstract
Causal inference on networks often encounters interference problems. The potentialoutcomes of a unit depend not only on its treatment but also on the treatments of
its neighbors in the network. The classic causal inference assumption of no interference
among units is untenable in networks, and many fundamental results in causal
inference may no longer hold in the presence of interference. To address interference
problems in networks, this thesis proposes a novel Network Embedding Matching
(NEM) framework for estimating causal effects under network interference. We recover
causal effects based on network structure in an observed network. Furthermore,
we extend the network interference from direct neighbors to k-hop neighbors. Unlike
most previous studies, which had strong assumptions on interference among units
in the network and did not consider network structure, our framework incorporates
network structure into the estimation of causal effects. In addition, our NEM framework
can be implemented in networks for randomized experiments and observational
studies. Our approach is interpretable and can be easily applied to networks. We
compare our approach with other existing methods in simulations and real networks,
and we show that our approach outperforms other methods under linear and nonlinear
network interference.
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