English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Methicillin-resistant Staphylococcus aureus (MRSA) is a type of bacteriaresistant to certain antibiotics, making it difficult to prevent MRSAinfections. Among decades of efforts to conquer infectious diseases caused byMRSA, many studies have been proposed to estimate the causal effects of closecontact (treatment) on MRSA infection (outcome) from observational data. Inthis problem, the treatment assignment mechanism plays a key role as itdetermines the patterns of missing counterfactuals -- the fundamental challengeof causal effect estimation. Most existing observational studies for causaleffect learning assume that the treatment is assigned individually for eachunit. However, on many occasions, the treatments are pairwisely assigned forunits that are connected in graphs, i.e., the treatments of different units areentangled. Neglecting the entangled treatments can impede the causal effectestimation. In this paper, we study the problem of causal effect estimationwith treatment entangled in a graph. Despite a few explorations for entangledtreatments, this problem still remains challenging due to the followingchallenges: (1) the entanglement brings difficulties in modeling and leveragingthe unknown treatment assignment mechanism; (2) there may exist hiddenconfounders which lead to confounding biases in causal effect estimation; (3)the observational data is often time-varying. To tackle these challenges, wepropose a novel method NEAT, which explicitly leverages the graph structure tomodel the treatment assignment mechanism, and mitigates confounding biasesbased on the treatment assignment modeling. We also extend our method into adynamic setting to handle time-varying observational data. Experiments on bothsynthetic datasets and a real-world MRSA dataset validate the effectiveness ofthe proposed method, and provide insights for future applications.

A Look into Causal Effects under Entangled Treatment in Graphs: Investigating the Impact of Contact on MRSA Infection