most citedIdentifiability of causal effects with non-Gaussianity and auxiliary covariates

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

stat.ME2026

Mediation analysis with unmeasured confounding between parallel mediators and outcome

Kang Shuai, Lan Liu, Yangbo He +1

Mediation analysis extending beyond single mediators has gained significant attention in recent years. However, related methods often assume the absence of unmeasured mediator-outc…

stat.ME2026

Identifying Causal Effects Using Instrumental Variables from the Auxiliary Dataset

Kang Shuai, Shanshan Luo, Wei Li +1

Instrumental variable approaches have gained popularity for estimating causal effects in the presence of unmeasured confounders. However, the availability of instrumental variables…

stat.ME20261 cited

Identifiability of causal effects with non-Gaussianity and auxiliary covariates

Kang Shuai, Shanshan Luo, Yue Zhang +2

Assessing causal effects in the presence of unmeasured confounding is challenging. Although auxiliary variables, such as instrumental variables, are commonly used to identify causa…

stat.ME2026

Bidirectional causal inference for binary outcomes in the presence of unmeasured confounding

Yafang Deng, Kang Shuai, Shanshan Luo

Bidirectional causal relationships arising from mutual interactions between variables are commonly observed within biomedical, econometrical, and social science contexts. When such…

stat.ME2025

Identification and estimation of causal peer effects using instrumental variables

Shanshan Luo, Kang Shuai, Yechi Zhang +2

In social science researches, causal inference regarding peer effects often faces significant challenges due to homophily bias and contextual confounding. For example, unmeasured h…