Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Heng, Siyu"'
Autor:
Wang, Pengyun, Huang, Ping, Jin, Yifan, Shen, Yanxin, Shahawy, Omar El, Ham, Dae Woong, O'Meara, Wendy P., Heng, Siyu
According to the WHO, in 2021, about 32% of pregnant women in sub-Saharan Africa were infected with malaria during pregnancy. Malaria infection during pregnancy can cause various adverse birth outcomes such as low birthweight. Over the past two decad
Externí odkaz:
http://arxiv.org/abs/2409.19314
Autor:
Zhang, Jeffrey, Heng, Siyu
Matching is a commonly used causal inference study design in observational studies. Through matching on measured confounders between different treatment groups, valid randomization inferences can be conducted under the no unmeasured confounding assum
Externí odkaz:
http://arxiv.org/abs/2409.12848
Matching is a commonly used causal inference framework in observational studies. By pairing individuals with different treatment values but with the same values of covariates (i.e., exact matching), the sample average treatment effect (SATE) can be c
Externí odkaz:
http://arxiv.org/abs/2409.11701
Graph domain adaptation has recently enabled knowledge transfer across different graphs. However, without the semantic information on target graphs, the performance on target graphs is still far from satisfactory. To address the issue, we study the p
Externí odkaz:
http://arxiv.org/abs/2409.08946
Graph pooling has gained attention for its ability to obtain effective node and graph representations for various downstream tasks. Despite the recent surge in graph pooling approaches, there is a lack of standardized experimental settings and fair b
Externí odkaz:
http://arxiv.org/abs/2406.09031
Autor:
Heng, Siyu, Kang, Hyunseung
In matched observational studies with binary treatments, the Rosenbaum bounds framework is arguably the most widely used sensitivity analysis framework for assessing sensitivity to unobserved covariates. Unlike the binary treatment case, although wid
Externí odkaz:
http://arxiv.org/abs/2403.14152
In causal inference, matching is one of the most widely used methods to mimic a randomized experiment using observational (non-experimental) data. Ideally, treated units are exactly matched with control units for the covariates so that the treatments
Externí odkaz:
http://arxiv.org/abs/2311.11216
Design-based causal inference, also known as randomization-based or finite-population causal inference, is one of the most widely used causal inference frameworks, largely due to the merit that its validity can be guaranteed by study design (e.g., ra
Externí odkaz:
http://arxiv.org/abs/2310.18556
Matching is a widely used causal inference study design in observational studies. It seeks to mimic a randomized experiment by forming matched sets of treated and control units based on proximity in covariates. Ideally, treated units are exactly matc
Externí odkaz:
http://arxiv.org/abs/2308.02005
Publikováno v:
In Contemporary Clinical Trials Communications October 2024 41
