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pro vyhledávání: '"Luo, ShanShan"'
In clinical trials, principal stratification analysis is commonly employed to address the issue of truncation by death, where a subject dies before the outcome can be measured. However, in practice, many survivor outcomes may remain uncollected or be
Externí odkaz:
http://arxiv.org/abs/2406.10554
Recently, interest has grown in the use of proxy variables of unobserved confounding for inferring the causal effect in the presence of unmeasured confounders from observational data. One difficulty inhibiting the practical use is finding valid proxy
Externí odkaz:
http://arxiv.org/abs/2405.16130
To evaluate a single cause of a binary effect, Dawid et al. (2014) defined the probability of causation, while Pearl (2015) defined the probabilities of necessity and sufficiency. For assessing the multiple correlated causes of a binary effect, Lu et
Externí odkaz:
http://arxiv.org/abs/2404.05246
In observational studies, covariates with substantial missing data are often omitted, despite their strong predictive capabilities. These excluded covariates are generally believed not to simultaneously affect both treatment and outcome, indicating t
Externí odkaz:
http://arxiv.org/abs/2402.14438
There is growing interest in exploring causal effects in target populations via data combination. However, most approaches are tailored to specific settings and lack comprehensive comparative analyses. In this article, we focus on a typical scenario
Externí odkaz:
http://arxiv.org/abs/2311.00528
Unmeasured confounding presents a common challenge in observational studies, potentially making standard causal parameters unidentifiable without additional assumptions. Given the increasing availability of diverse data sources, exploiting data linka
Externí odkaz:
http://arxiv.org/abs/2309.08199
Instrumental variable approaches have gained popularity for estimating causal effects in the presence of unmeasured confounding. However, the availability of instrumental variables in the primary population is often challenged due to stringent and un
Externí odkaz:
http://arxiv.org/abs/2309.02087
Assessing causal effects in the presence of unmeasured confounding is a challenging problem. It has been previously shown that the causal effect is identifiable when noise variables of the treatment and outcome are both non-Gaussian under linear mode
Externí odkaz:
http://arxiv.org/abs/2304.14895
Publikováno v:
In International Review of Economics and Finance January 2025 97
Autor:
Xu, Hui a, Dai, Wenjuan b, Xiong, Zhengyu a, Huang, NaNa a, Wang, Yanrui a, Yang, Zhe a, Luo, Shanshan a, c, ⁎⁎, Wu, Jielian a, ⁎
Publikováno v:
In Developmental and Comparative Immunology January 2025 162