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Causal Inference Under Network Interference: Network Embedding Matching.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Causal Inference Under Network Interference: Network Embedding Matching.
Author:	Zhang, Xu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2023
Description:	179 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Notes:	Advisor: Airoldi, Edoardo M.;Karwa, Vishesh.
Contained By:	Dissertations Abstracts International84-11B.
Subject:	Statistics.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30249485
ISBN:	9798379546045

Causal Inference Under Network Interference: Network Embedding Matching.
Zhang, Xu.

Causal Inference Under Network Interference: Network Embedding Matching. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 179 p.

Source: Dissertations Abstracts International, Volume: 84-11, Section: B.

Thesis (Ph.D.)--Temple University, 2023.

This item must not be sold to any third party vendors.

Causal inference on networks often encounters interference problems. The potential outcomes 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.

ISBN: 9798379546045Subjects--Topical Terms:

182057
Statistics.
Subjects--Index Terms:

Causal inference

Causal Inference Under Network Interference: Network Embedding Matching.
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245 1 0 $a Causal Inference Under Network Interference: Network Embedding Matching.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 179 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
500 $a Advisor: Airoldi, Edoardo M.;Karwa, Vishesh.
502 $a Thesis (Ph.D.)--Temple University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Causal inference on networks often encounters interference problems. The potential outcomes 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.
590 $a School code: 0225.
650 4 $a Statistics. $3 182057
650 4 $a Computer science. $3 199325
653 $a Causal inference
653 $a Data science
653 $a Interference
653 $a Machine learning
653 $a Network analysis
653 $a Network effects
690 $a 0463
690 $a 0984
690 $a 0800
710 2 $a Temple University. $b Statistics. $3 966794
773 0 $t Dissertations Abstracts International $g 84-11B.
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791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30249485