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Causal Inference Under Network Interference : = Network Embedding Matching.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Causal Inference Under Network Interference :/
Reminder of title:	Network Embedding Matching.
Author:	Zhang, Xu.
Description:	1 online resource (179 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:	Dissertations Abstracts International84-11B.
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9798379546045

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

Causal Inference Under Network Interference :Network Embedding Matching. - 1 online resource (179 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379546045Subjects--Topical Terms:

556824
Statistics.
Subjects--Index Terms:

Causal inferenceIndex Terms--Genre/Form:

554714
Electronic books.

Causal Inference Under Network Interference : = Network Embedding Matching.
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502 $a Thesis (Ph.D.)--Temple University, 2023.
504 $a Includes bibliographical references
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.
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538 $a Mode of access: World Wide Web
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653 $a Data science
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653 $a Machine learning
653 $a Network analysis
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